Recently, the Digital Twin (DT) technology has procured a lot of attention because of its applicability in the manufacturing and space industries. The DT environment involves the formation of a clone of the tangible object to perform simulations in the virtual space. The combination of conceptual development, predictive maintenance, real-time monitoring, and simulation characteristics of DT has increased the utilization of DT in different scenarios, such as medical environments, healthcare, manufacturing industries, aerospace, etc. However, these utilizations have also brought serious security pitfalls in DT deployment. Towards this, several authentication protocols with different security and privacy features for DT environments have been proposed. In this article, we first review a recently proposed two-factor authentication protocol for DT environments that utilizes the blockchain technology. However, the analyzed scheme is unable to offer the desirable security and cannot withstand various security attacks like offline password-guessing attack, smart card stolen attack, anonymity property, and known session-specific temporary information attack. We also demonstrate that an attacker can impersonate the analyzed protocol’s legal user, owner, and cloud server. To mitigate these security loopholes, we devise an effective three-factor privacy-preserving authentication scheme for DT environments. The proposed work is demonstrated to be secure by performing the informal security analysis, the formal security analysis using the widely recognized Burrows-AbadiNeedham (BAN) logic, and the Real-or-Random (ROR) model. A detailed comparative study on existing competing schemes including the analyzed scheme demonstrates that the devised framework furnishes better security features while also having lower computation costs and comparable communication costs than the existing schemes.

Fully Homomorphic Encryption (FHE) is a rapidly developing technology that enables computation directly on encrypted data, making it a compelling solution for security in cloud-based systems. In addition, modern FHE schemes are believed to be resistant to quantum attacks. Although FHE offers unprecedented potential for security, current implementations suffer from prohibitively high latency. Finite field arithmetic operations, particularly the multiplication of high-degree polynomials, are key computational bottlenecks. The parallel processing capabilities provided by modern Graphical Processing Units (GPUs) make them compelling candidates to target these highly parallelizable workloads. In this article, we discuss methods to accelerate polynomial multiplication with GPUs, with the goal of making FHE practical.

Existing Single Sign-On (SSO) access control systems typically rely on the traditional Protocols requiring additional authentication mechanism and/or identity providers. As the growing demand in outsourcing system resources such as data and applications to the cloud platform, implementing traditional SSO models to support efficient and fine-grained access control for multi-user and multi-application environment is not practical. In this paper, we propose a blockchain-based identification and access management (IAM) scheme called D2-IAM to provide strong security measures for controlling SSO access to resources in the cloud. At a core of D2-IAM, core access control processes are done by the smart contracts and blockchain where the access transactions are well retained for the accountability. In our system, the SSO Authentication is based on the highest authentication level and the hashed-based token management. Owing to the autonomous authentication management, the communication overhead regarding the interaction with identity providers and third-party verification mechanism for multi-system authentication is minimized. For the authorization system, D2-IAM enables fine-grained access through the access policy modeled in the document database written and enforced to each customer. Finally, we conducted the experiments on Google cloud to show that our D2-IAM system is efficient for the implementation. The performance test showed that our proposed system was approximately 4 times efficient than the average processing time of three existing works. Lorem ipsum dolor, sit amet consectetur adipisicing elit. Omnis, cumque!

Software-Defined Network (SDN) represents a new network paradigm. Unlike conventional networks, SDNs separate control planes and data planes. The function of a data plane is enabled using switches, whereas that of a control plane is facilitated by a controller. The controller learns network topologies and makes traffic forwarding decisions. However, some serious vulnerabilities are gradually exposed in the topology management services of current SDN controller designs. These vulnerabilities mainly exist in host tracking and link discovery services. Attackers can exploit these weak points to poison the network topology information in SDN controllers. In this study, a novel solution is proposed to defend against topology poisoning attacks. By analyzing the existing Topology attack principles and threat models, this work constructs legal conditions for host migration to detect host hijacking attacks. The checking of the Link Layer Discovery Protocol (LLDP) source and integrity is designed to defend against link fabrication attacks. A relay-type link fabrication attack detection method based on entropy is also designed. Results show that the proposed solution can effectively detect existing topological attacks and provide complete and comprehensive topological security protection.

The sharing of data is becoming increasingly important for the process and manufacturing industries that are using data-driven models and advanced analysis to assess production performance and make predictions, e.g., on wear and tear. In such environments, access to data needs to be accurately controlled to prevent leakage to unauthorized users while providing easy to manage policies. Data should further be shared with users outside trusted domains using encryption. Finally, means for revoking access to data are needed. This paper provides a survey on attribute-based approaches for access control to data, focusing on policy management and enforcement. We aim to identify key properties provided by attribute-based access control (ABAC) and attribute-based encryption (ABE) that can be combined and used to meet the abovementioned needs. We describe such possible combinations in the context of a proposed architecture for secure data sharing. The paper concludes by identifying knowledge gaps to provide direction to future research on attribute-based approaches for secure data sharing in industrial contexts.

One of the fundamental construction blocks in safeguarding group communications is group key establishment protocols. Group key agreement protocols are more suitable for distributed environments where the participant from various places can agree upon the group key. Group key related information is distributed to various group participants, mostly using techniques such as using polynomials, bilinear pairing, and secret sharing scheme (SSS). Out of these, secret sharing schemes are more efficient compared to other techniques. Recent growth in Internet of things (IoT) related applications stresses the need for such key agreement protocols in resource-constrained environments. Elliptic curves are quite popular in resource-constrained environments to produce enough security with smaller key sizes. Elliptic curve secret sharing scheme (ECSSS) is proposed in this paper for resource-constrained environments. Using the same scheme, Authenticated distributed group key agreement protocol using Elliptic curve secret sharing scheme (ADGKAP) is proposed, which can be used as a group key agreement protocol, and the group key related information is shared using ECSSS. To the best of our knowledge, we know no group key agreement protocol in the literature that uses ECSSS in a distributed environment. Our proposed ADGKAP provides equal security with less key size, storage space, faster computation, and less computational cost without compromising on the number of rounds compared to existing schemes. This scheme security relies on the Elliptic curve discrete logarithm problem (ECDLP), and the security analysis of the scheme is discussed.

The implementation of the Internet of Things and 5G communications has pushed centralized cloud computing toward edge computing resulting in a paradigm shift in computing. Edge computing allows edge devices to offload their overflowing computing tasks to edge servers. This procedure may completely exploit the edge server’s computational and storage capabilities and efficiently execute computing operations. However, transferring all the overflowing computing tasks to an edge server leads to long processing delays and surprisingly high energy consumption for numerous computing tasks. Aside from this, unused edge devices and powerful cloud centers may lead to resource waste. Thus, hiring a collaborative scheduling approach based on task properties, optimization targets, and system status with edge servers, cloud centers, and edge devices is critical for the successful operation of edge computing. The primary motivation behind this study is to introduce the most recent advancements related to resource scheduling techniques and address the existing limitations. Firstly, this paper presents a novel taxonomy of resource scheduling in edge computing that includes applications, computational platforms, algorithm paradigms, and objectives. Secondly, it briefly summarizes the edge computing architecture for information and task processing. Resource scheduling techniques are then discussed and compared based on four collaboration modes. According to the literature surveyed, we briefly looked at the fairness and load balancing indicators in scheduling. Additionally, the survey conducted provides a comprehensive review of the state-of-the-art edge computing issues and challenges. Finally, this paper highlights deep learning, multi-objective optimization, and using green resources as key techniques for future directions.

Fueled by the explosive growth of edge smart IoT devices, edge storage systems (ESS) have emerged as a new paradigm to support the efficient access of massive edge data. ESS can greatly alleviate the burden of cloud center and enhance the Quality of Experience (QoE) for users. However, despite the remarkable progress of ESS, it still faces the challenges of how to improve the systems fault tolerance ability and efficiency. Therefore, designing a secure and efficient fault-tolerant storage scheme is urgent and indispensable. Unfortunately, existing fault-tolerant schemes for ESS still retain various drawbacks, including: high edge storage overhead, hard to protect the edge data privacy and low data writing performance. Motivated by this, we propose a secure cloud-edge collaborative fault-tolerant storage scheme and its data writing optimization method. Precisely, we first propose a Hierarchical Cloud-Edge Collaborative Fault-Tolerant Storage (HCEFT) model to achieve system robustness, low edge storage overhead, and edge data privacy security. We further optimized the writing process of HCEFT by designing a data writing optimization method called ECWSS (Erasure Code data Writing method based on Steiner tree and SDN) to achieve a better trade-off between the data writing time and traffic consumption. Finally, Comprehensive comparison and extensive experiments show that our scheme can achieve better data robustness, availability and security. Moreover, the writing optimization method can reduce 13%-67% data write time and 20%-62% network traffic consumption while providing better network load balance performance.

The Industrial Internet of Things (IIoT) collects a large amount of data through various types of sensors and intelligently processes this data using cloud computing, which is flexible, efficient, and cost-effective. Since IIoT data is stored on the cloud service provider’s server, the data must be encrypted to protect the user’s privacy. However, the encrypted data faces the search problem, which is usually solved by Public Key Encryption with Keyword Search (PEKS). In addition, most existing PEKS schemes are vulnerable to Inside Keyword Guessing Attacks (IKGA). Recently, some certificate less public key authenticated encryption with keyword search (CLPEKS) schemes have been proposed, which not only avoid the problems of certificate management and key escrow but can also resist IKGA. However, most of them rely on the expensive bilinear pairing. To overcome these problems, in this paper we propose a pairing-free CLPEKS scheme. The security of the proposed scheme is proved in the random oracle model. The analysis results show that the proposed scheme has better overall performance in terms of computational cost, communication cost and security properties.

As cloud computing and AI as a Service are provided, it is increasingly necessary to deal with privacy sensitive data. To deal with the sensitive data, there are two cases of outsourcing process: i) many clients participate dynamically ii) many clients are pre-determined. The solutions for protecting sensitive data in both cases are the multi-key homomorphic encryption (MKHE) scheme and the threshold multi-key homomorphic encryption (TMKHE) scheme. However, these schemes may be difficult for clients with limited resources to perform MKHE and TMKHE. In addition, due to the large size of the evaluation keys, in particular multiplication and rotation keys, the communication between the clients and server that provide outsourcing service increases. Also, the size of the evaluation keys that the server must hold is tremendous, in particular, for the multiplication and rotation keys, which are essential for bootstrapping operation. In this paper, we propose a variant of MKHE and TMKHE with reduced evaluation keys. To reduce the size of the evaluation keys, we propose a variant of ring learning with errors (RLWE), called RLWE reusing errors (ReRLWE). ReRLWE generates other components by reusing the error that is used when generating an RLWE sample. We prove that RLWE can be reduced to ReRLWE and propose modified evaluation keys under the ReRLWE assumption, which are the modified multiplication and rotation keys.

We propose a credible alliance-chain-based public auditing scheme (CACPA) to properly address the trust problem of third-party auditor (TPA) in the traditional audit scheme for cloud storage. Based on the non-tamperability and traceability of blockchain, we design a novel incentive mechanism to enforce honest and reliable behaviors of TPA. The basic idea is to organize TPA as a group of blockchain nodes conducting mutual surveillance. Any behavior of a TPA node will be inspected by other nodes to maintain good reputation of the group, and malicious behaviors will bring severe punishments. Accordingly, we develop system model of the proposed CACPA, as well as smart contracts to deal with transactions-related issues, such as dispute resolution. Finally, performance evaluation validates that our scheme enjoys acceptable efficiency and suits for real-world applications.

Attribute-based encryption is a valuable technique for ensuring data privacy and confidentiality in the realm of cloud computing. Using this cryptographic primitive, the data owners can securely store and share data within the cloud environment. On the other hand, in recent years, extensive advances have been made in quantum processors, which have raised hopes of solving certain mathematical problems includes factoring integers and computing discrete logarithms of large numbers. The advent of quantum computers has posed a significant security threat to existing cryptographic protocols. The existing post-quantum attribute based encryption schemes have not satisfied the essential features such as verifiability, user privacy and user revocability, simultaneously. In this paper, we present the first secure, practical and post-quantum attribute based encryption scheme based on rank metric codes. Our scheme enjoys all mentioned features due to utilization of low rank parity check codes. The proposed scheme provides security against chosen plaintext attacks in the standard model, as well as resistance against reaction attacks as a kind of chosen cipher text attacks.

As the field of information technology expands, there is a huge need for cloud service providers (CSP). CSP’s vast solutions and services support Cloud, IoT, Fog, and Edge computing. In today’s competitive cloud market, customer satisfaction is critical more than ever. CSP and consumer satisfaction with service level agreement (SLA) fulfilment have always been given more attention. As a result of signing SLA and CSP agreements to supply resources in high demand, customers are now experiencing issues with resource delivery. Cloud and heterogeneous environments necessitate an intelligent recommender and negotiation agent model (IRNAM) to handle responsibilities in the current system. The Recommender system recommends CSP as per users’ priorities, which eases the filtration process. The negotiation process provided by IRNAM ensures that users’ choices are prioritized with maximum jobs to CSP. IRNAM keeps track of the most critical metrics and can reach decisions quickly and for the best possible deal. It uses an analytical concession algorithm that analyses consumer and CSP choices to find a reliable, secure server with the simplest solution. The negotiation process uses users and CSP choice metrics, performance factors, evaluation measures, and success factors in the best execution time to decide. IRNAM provides a flexible and valuable way for selecting CSP and negotiating for services on the user’s terms while considering CSP satisfaction.

As one fundamental data-mining problem, ANN (approximate nearest neighbor search) is widely used in many industries including computer vision, information retrieval, and recommendation system. LSH (Local sensitive hashing) is one of the most popular hash-based approaches to solve ANN problems. However, the efficiency of operating LSH needs to be improved, as the operations of LSH often involve resource-consuming matrix operations and high-dimensional large-scale datasets. Meanwhile, for resource-constrained devices, this problem becomes more serious. One way to handle this problem is to outsource the heavy computing of high-dimensional large-scale data to cloud servers. However, when a cloud server responsible for computing tasks is untrustworthy, some security issues may arise. In this study, we proposed a cloud server-aided LSH scheme and the application model. This scheme can perform the LSH efficiently with the help of a cloud server and guarantee the privacy of the client’s information. And, in order to identify the improper behavior of the cloud server, we also provide a verification method to check the results returned from the cloud server. Meanwhile, for the implementation of this scheme on resourceconstrained devices, we proposed a model for the real application of this scheme. To verify the efficiency and correctness of the proposed scheme, theoretical analysis and experiments are conducted. The results of experiments and theoretical analysis indicate that the proposed scheme is correct, verifiable, secure and efficient.

In the mobile edge computing (MEC) environment, edge servers with storage and computing resources are deployed at base stations within users’ geographic proximity to extend the capabilities of cloud computing to the network edge. Edge storage system (ESS), is comprised by connected edge servers in a specific area, which ensures low-latency services for users. However, high data storage overheads incurred by edge servers’ limited storage capacities is a key challenge in ensuring the performance of applications deployed on an ESS. Data deduplication, as a classic data reduction technology, has been widely applied in cloud storage systems. It also offers a promising solution to reducing data redundancy in ESSs. However, the unique characteristics of MEC, such as edge servers’ geographic distribution and coverage, render cloud data deduplication mechanisms obsolete. In addition, data distribution must be balanced over edge storage systems to accommodate future data demands, which cannot be undermined by data deduplication. Thus, balanced edge data deduplication (BEDD) must consider deduplication ratio, data storage benefits, and resource balance systematically under the latency constraint. In this article, we model the novel BEDD problem formally and prove its N P-hardness. Then, we propose an optimal approach for solving the BEDD problem exactly in small-scale scenarios and a sub-optimal approach to solve large-scale BEDD problems with a theoretical performance guarantee. Extensive and comprehensive experiments conducted on a real-world dataset demonstrate the significant performance improvements of our approaches against four representative approaches.

Nowadays, mobile devices can run a wide range of programs, and they all require more and more processing power. Due to their limited resources, mobile devices often make use of cloud computing ′s offloading features to do more complex tasks. The offloading problem in Mobile Cloud Computing (MCC) is the task scheduling problem, which entails deciding where to dump work to maximize its value. The task scheduling problem in MCC is an NP-hard problem because of the difficulty in moving resources and the size of the search space required to find the ideal scheduler, making the use of extensive search techniques impractical. For this reason, metaheuristic search strategies are provided, to yield a best-case or near-best-case scenario in terms of job completion time and energy savings. This work provides a non-dominated multi-objective strategy based on the Harris Hawks Optimization (HHO) technique called Hybrid Multi-objective Harris Hawks Optimization (HMHHO) to handle the described issue in MCC. The objectives of this research were allocating jobs from mobile source nodes to processors in the public cloud, cloud patches, and processors in mobile resources. In comparison to the other four algorithms—the Genetic Algorithm (GA), the Ant Colony Optimization (ACO), the Particle Swarm Optimization (PSO), and the Cuckoo Search Algorithm (CSA) the proposed method completes jobs faster and uses less energy on average.

Revocable identity-based encryption (RIBE) is an extension of identity-based encryption (IBE) and it supports efficient revocation of private keys. In the past, many efficient RIBE schemes have been proposed, but research on efficiently delegating the generation of update keys to a cloud server is somewhat insufficient. In this paper, we newly introduce the concept of delegated RIBE (DRIBE) that can delegate the generation of update keys to the semi-trusted cloud server and define the security models of DRIBE. Next, we propose a DRIBE scheme by generically combining a hierarchical IBE (HIBE) scheme, an identitybased broadcast encryption (IBBE) scheme, and a collision-resistant hash function. In addition, we propose a DRIBE-INC scheme that generates an occasional base update key and a periodic incremental update key to reduce the size of update keys in our DRIBE scheme.

In the previous works, to further provide the continuous leakage resilience for the identity-based encryption scheme, a new cryptography primitive, called updatable identity-based hash proof system (U-IB-HPS), was proposed. However, most of the existing constructions have some deficiencies, they either do not have perfect key update function or the corresponding security with tight reduction relies on a non-static complexity assumption. To address the above problems, a new construction of U-IB-HPS is created, and the corresponding security of our system is proved based on the static complexity assumption. Also, the corresponding comparisons and analysis of performances show that our proposal not only achieves the perfect key update function and the anonymity, but also has the tight security reduction. In additional, our proposal achieves the same computational efficiency as other previous systems. To further illustrate the practical function of U-IB-HPS, a generic method of non-interactive data authorization protocol with continuous leakage resilience is designed by employing U-IB-HPS as an underlying tool, which can provide continuous leakage-resilient data authorization function for the cloud computing. Hence, the application field of U-IB-HPS is further extended through our study.

Students learning performance is one of the core components for assessing any educational systems. Student’s performance is very crucial in tackling issues of learning process and one of the important matters to measure learning outcomes. The ability to use data knowledge to improve education systems has led to the development of the field of research known as educational data mining (EDM). EDM is the creation of techniques to investigate data gathered from educational settings, allowing for a more thorough and accurate understanding of students and the improvement of educational outcomes for them. The use of machine learning (ML) technology has increased significantly in recent years. Researchers and teachers can use the measurements of success, failure, dropout, and more provided by the discipline of data mining in education to predict and simulate education processes. Therefore, this work presents an analysis of student’s performance using data mining methods. The paper presents both clustering and classification techniques to identify the impact of students performance at early stage with on the GPA. For the clustering technique, the paper uses dimensionality reduction mechanism by T-SNE algorithm with various factors at early stage such as admission scores and first level courses, academic achievement tests (AAT) and general aptitude tests (GAT) in order to explore the relationship between these factors and GPA’s. For the classification technique, the paper presents experiments on different machine learning models on predicting student performance at early stages using different features including courses’ grades and admission tests’ scores. We use different assessment metrics to evaluate the quality of the models. The results suggest that educational systems can mitigate the risks of student’s failures at the early stages.

Diabetes is a common chronic illness or absence of sugar in the blood. The early detection of this disease decreases the serious risk factor. Nowadays, Machine Learning based cloud environment acts as a vital role in disease detection. The people who belong to the rural areas are not getting the proper health care treatments. So, this research work proposed an automated eHealth cloud system for detecting diabetes in the earlier stage to decrease the mortality rate and provides health treatment facilities to rural peoples. Extreme Learning Machine (ELM) is a type of Artificial Neural Network (ANN) that has a lot of potential for solving classification challenges. This research work is consisting of several activities like feature normalization, feature selection and classification. We have employed principal component analysis (PCA) for feature selection and extreme learning machine (ELM) for classification. Finally, a cloud computing based environment with three numbers of virtual machines (vCPU-4, vCPU-8, and vCPU-16), is used for the detection of diabetes. The efficacy of the proposed model has been evaluated with the PIMA dataset in both standalone and cloud environments and achieved 90.57 % accuracy, 82.24 % sensitivity, 73.23 % specificity, and 75.03 % F-1 score with the virtual machine vCPU-16. The experimental results define the proposed model as superior to other state-of-art models with better classification accuracy and less number of features.

At present, three problems exist in searchable encryption in cloud storage services: firstly, most traditional searchable encryption schemes only support single-keyword search while fail to perform Boolean searches; even if a few schemes support Boolean searching, the storage efficiency is also unsatisfactory. Secondly, most existing schemes do not support dynamic keyword updates, so the update efficiency is low. Thirdly, most existing schemes cannot meet all demands of users, to perform rankable searching over search files according to the importance of keywords. To solve these problems, a rankable Boolean searchable encryption scheme supporting dynamic updates in a cloud environment (RBDC) is proposed. By using Paillier and GM encryption algorithms, secure indices supporting dynamic updating are established. Based on applicable knowledge gleaned from cryptography and set theory, the indices of keyword intersections and the intersection search trapdoors are constructed to achieve multi-keyword Boolean search. With assistance of the SCP, score indices of each file are constructed according to the TF-IDF index, which allow ranking of files. Security analysis proofs that our scheme can ensure security in the known ciphertext model and the known background model. Experimental results prove that the scheme improves the search efficiency and the index storage efficiency.

With the development of big data and deep learning technology, big data and deep learning technology have also been applied to the marketing field, which was a part of business administration. Customer churn management is one of the most important areas of marketing. In this paper, we proposed a method to prevent customer churn and increase purchase conversion rate by issuing customized discount coupons to customers with high churn rate based on big data in real time. After segmenting customer segments with two-dimensional segment analysis, a real-time churn rate estimation model based on clickstream data was generated for each segment. After that, we issued customized coupons to our customers. Finally, we tested the conversion rate and sales growth. A two-dimensional cluster analysis-based churn rate estimation combined with a recommendation system was found to be significantly more useful than the respective simple models. Using this proposed model, it is possible to increase sales by automatically estimating the customer’s churn probability and shopping propensity without the burden of marketing costs in the online shopping mall.

We present a solution for accountability in Cloud infrastructures based on blockchain. We show that, through smart contracts, it is possible to create an unforgeable log that can be used for auditing and automatic Service Level Agreement (SLA) verification. As a practical case study, we consider Cloud storage services and define interaction protocols for registering the outcome of each file operation in the blockchain. We developed a prototype implementation that runs on the GoQuorum, Hyperledger Besu, and Polygon blockchains, using different consensus protocols. Using a dedicated testbed, we discuss the performance of our implementation in terms of latencies, error rates and gas usage. Results demonstrate the viability of our approach over permissioned blockchains, with better performance for the Polygon and GoQuorum Raft decentralized systems. Our implementation enables interoperability, given that it is supported by the Ethereum Virtual Machine which currently is underlying several blockchain platforms.

Personal data is becoming increasingly valuable in business, as the insights that can be obtained from data processing continue to improve. However, it also can cause adverse effects on individuals. To improve data quality while satisfying privacy compliance, companies now have focused on collecting informed consent from individuals to directly handle personal data without applying any privacy-preserving techniques. Even though the companies obtain consent to use personal data, to improve transparency and accountability to ensure that companies deal with personal data according to consent, it is necessary for a system to comply with privacy requirements. Therefore, this paper proposes a new consent-based privacy-compliant personal data-sharing system that considers personal data-sharing flows and requirements obtained from enterprises and privacy frameworks, respectively. By analyzing a general process and the roles of actors for data sharing in enterprise environments according to standard privacy frameworks, this paper has proposed system requirements, system architecture, and detailed procedure for a consent-based privacy compliant processing method that considers compliance checking as well as consent checking. To show the feasibility of the proposed system, this paper demonstrates a prototype and the performance analysis in the lab and real-world environments.

People use web search engines to retrieve information from the World Wide Web. Search engines maintain query logs to refine retrieved information for personalized web search. The query log includes information of users, both sensitive and non-sensitive. If utilized against an individual, the query log may pose privacy concerns and reveal a lot of information about them. In recent years many private web search (PWS) schemes have been proposed to realize privacy-preserving web search. Although each PWS scheme claims to have unique features for attaining web search privacy (WSP), no study explains which private web search characteristics should be considered when building and utilizing a PWS scheme. There are two objectives of this article. In the first part of the article, we present a novel PWS scheme that uses a proxy-query-based query obfuscation approach. Proxy-query-based query obfuscation is a new study topic in PWS research. It provides an IR facility for retrieving information from web search engines via proxy queries. One clear advantage of the proposed scheme is that users do not issue true queries to search engines for retrieving information. The article’s second objective is to define the characteristics of PWS and analyze modern PWS schemes on these characteristics. We analyzed proposed and modern PWS schemes on the PWS characteristics. The analysis demonstrated only the proposed PWS scheme achieves all characteristics. Existing PWS systems have been discovered vulnerable to WSP attacks since they do not meet all PWS characteristics.

We show how to implement a deniable encryption method from secret sharing. Unlike the related concept of honey encryption, which employs a preprocessing step in symmetric encryption to re-shape the distribution of a plaintext towards making the real plaintext indistinguishable from a ciphertext for a fake message, we can avoid both, computational intractability assumptions and preprocessing of the data. This accomplishes deniability against an attacker that can force decryptions, and it can brute-force break a ciphertext with sufficient computational power. Following the concept of plausible deniability, we herein have different decryption keys to open up distinct plaintexts from within the same ciphertext. For instance, a plaintext revealed from a ciphertext with a key which was shared by a victim under duress, will convince the attacker that it is real, while the actual secret remains unnoticed. False Bottom Encryption constructs a symmetric scheme (in the sense of using the same key to encrypt and decrypt) that shares the properties of both honey encryption and deniable encryption. We specifically formalize and differentiate ‘‘deniable’’ from ‘‘plausibly deniable’’ as a security feature, showing how plausible deniability falls back to (only) deniability, depending on the plaintext distribution. Our scheme is simple, lightweight to implement and efficient in terms of encryption and decryption, and is based on secret sharing. As such, we do not rely on computational intractability. We corroborate the construction by giving numeric examples and providing implementations of the method as a Jupyter notebook supplementary to this work.

The explosive growth of player-versus-player games and tournaments has catapulted esports games into a rapidly expanding force in the gaming industry. However, novice and armature players’ voices are often inadvertently overlooked because of a lack of effective analytical methods, despite the close collaboration between professional esports teams and operators. To ensure the quality of esports game services and establish a balanced gaming environment, it is essential to consider the opinions of unprofessional players and comprehensively analyze their reviews. This study proposes a new framework for analyzing esports reviews of players. It incorporates two key components: topic modeling and sentiment analysis. Utilizing the Latent Dirichlet Allocation (LDA) algorithm, the framework effectively identifies diverse topics within reviews. These identified topics were subsequently employed in a prevalence analysis to uncover the associations between players’ concerns and various esports games. Moreover, it leverages cutting-edge Bidirectional Encoder Representations from Transformers (BERT) in conjunction with a Transformer (TFM) downstream layer, enabling accurate detection of players’ sentiments toward different topics. We experimented using a dataset containing 1.6 million English reviews collected up to December 2021 for four esports. The experimental results demonstrated that the proposed framework can efficiently identify players’ concerns and reveal interesting keywords underlying their reviews. Consequently, it provides precise insights and valuable customer feedback to esports game operators, enabling them to enhance their services and provide an improved gaming experience for all players.

The rising popularity of online shopping has led to a steady stream of new product evaluations. Consumers benefit from these evaluations as they make purchasing decisions. Many research projects rank products using these reviews, however, most of these methodologies have ignored negative polarity while evaluating products for client needs. The main contribution of this research is the inclusion of negative polarity in the analysis of product rankings alongside positive polarity. To account for reviews that contain many sentiments and different elements, the suggested method first breaks them down into sentences. This process aids in determining the polarity of products at the phrase level by extracting elements from product evaluations. The next step is to link the polarity to the review’s sentence-level features. Products are prioritized following user needs by assigning relative importance to each of the polarities. The Amazon review dataset has been used in the experimental assessments so that the efficacy of the suggested approach can be estimated. Experimental evaluation of PRUS utilizes rank score (RS) and normalized discounted cumulative gain (nDCG) score. Results indicate that PRUS gives independence to the user to select recommended list based on specific features with respect to positive or negative aspects of the products.

Serverless computing has seen rapid growth, thanks to its adaptability, elasticity, and deployment agility, embraced by both cloud providers and users. However, this surge in serverless adoption has prompted a reevaluation of security concerns and thus, searchable encryption has emerged as a crucial technology. This paper explores the Searchable Encryption as a Service (SEaaS) and introduces an innovative privacy-preserving Multiple Keyword Searchable Encryption (MKSE) scheme within a server less cloud environment, addressing previously unmet security goals. The proposed scheme employs probabilistic encryption and leverages fully homomorphic encryption to enable operations on cipher text, facilitating searches on encrypted data. Its core innovation lies in the use of probabilistic encryption for private multi keyword searches. To validate its practicality, we deploy the scheme on the public cloud infrastructure, ‘‘Contabo,’’ and conduct rigorous testing on a real-world dataset. The results demonstrate that our novel scheme successfully preserves the privacy of search queries and access patterns, achieving robust security. This research contributes to the field of server less cloud security, particularly in the context of searchable encryption, by providing a refined solution for safeguarding data while maintaining usability in a server less computing landscape.

A successful application of an Internet of Things (IoT) based network depends on the accurate and successful delivery of data collected from numerous sources. A significant concern in IoT systems arises when end-users do not have sufficient transparency and are unaware of any potential data manipulation and risk in each step involved in data propagation. One potential solution is to integrate security metadata in IoT-based security-aware provenance graphs that provides better transparency with security awareness at each step of data propagation. In this paper, we integrate security metadata into the provenance graph with predefined security policies. We design a hypothetical IoT-Health scenario with possible threats: node cloning, fault packet injection, denial of service, unauthorized access, and malicious code injection. We simulate these threats in six cases to identify relevant risks. Our findings show how a security-aware provenance graph can offer end users greater transparency and security awareness by identifying failed signature verification (case 1), denial of service (case 2), unauthorized access (case 3), intrusion detection (case 4), missing WAF (case 5), and permission violation (case 6). We evaluate the transparency through obtaining authentication, integrity, availability and detecting underlying threats. Accordingly, this study promotes better risk assessment and decision-making for users with negligible performance overhead.

Relational Keyword Search (R-KwS) systems enable naive/informal users to explore and retrieve information from relational databases without knowing schema details or query languages. They take a keyword query, locate their corresponding elements in the target database, and connect them using information on PK/FK constraints. Although there are many such systems in the literature, most of them only support queries with keywords referring to the contents of the database and just very few support queries with keywords refering the database schema. We propose Lathe, a novel R-KwS that supports such queries. To this end, we first generalize the well-known concepts of Candidate Joining Networks (CJNs) and Query Matches (QMs) to handle keywords referring to schema elements and propose new algorithms to generate them. Then, we introduce two major innovations: a ranking algorithm for selecting better QMs, yielding the generation of fewer but better CJNs, and an eager evaluation strategy for pruning void useless CJNs. We present experiments performed with query sets and datasets previously experimented with state-of-theart R-KwS systems. Our results indicate that Lathe can handle a wider variety of queries while remaining highly effective, even for databases with intricate schemas.

Key encapsulation schemes in public key system (PKS) can be used to protect sensitive or private data. Unlike traditional PKS and identity-based PKS, certificate-based PKS (CB-PKS) not only avoids the establishment of complex public key infrastructures, but also does not encounter the key escrow problem. Anonymous multi-receiver certificate-based encryption (AMR-CBE) or anonymous multi-receiver certificate-based key encapsulation (AMR-CB-KE) scheme enables a sender to use multiple receivers’ public keys to perform one-time encryption process for a message and send the encrypted ciphertext to these receivers, while these receivers do not know the other receiver’s identity. However, the existing AMR-CBE and AMR-CB-KE schemes cannot resist side-channel attacks. Attackers with the ability of such attacks can continuously obtain part (several bits) of the secret keys and then calculate the complete secret keys. In such a case, such attacks make a cryptographic scheme (including AMR-CBE and AMR-CB-KE schemes) insecure. Leakage-resilient cryptography is an important research topic to resist side-channel attacks. In this paper, we propose the first leakage-resilient anonymous multi-receiver certificate-based key encapsulation (LR-AMR-CB-KE) scheme. Based on the discrete logarithm and hash function assumptions, we demonstrate the scheme has the indistinguishability of two ciphertexts against chosen ciphertext attacks (IND-CCA) and the anonymous indistinguishability of two identities against chosen ciphertext attacks (ANON-IND-CCA) for two types of attackers in CB-PKS settings.

According to the characteristics of massive, multi-source, heterogeneous, and rapid growth of book literature data information from the perspective of the met averse, in order to meet the requirements of efficient management and rapid retrieval such as standardized storage, effective extraction, and scientific library construction for unstructured massive and heterogeneous book in-formation, this study focuses on the normalization of multi-source heterogeneous massive book data, the construction of a warehouse model for book data in the metaverse perspective, and the query and optimization of book data. Systematic research and implementation were conducted to solve the problem of how to process, manage, and query multi-source heterogeneous massive book data in the metaverse, improving the utilization value and query efficiency of the data. This study utilized the semi-structured features of book text data to construct an extraction rule model for heterogeneous book data, and effectively extracted massive heterogeneous book information. Based on the HBase distributed storage structure and parallel computing technology, the storage scheme has been optimized and query efficiency has been improved to ensure efficient management and retrieval of massive heterogeneous book data. The experimental results show that compared with traditional methods, there are significant improvements in multiple aspects such as the accuracy and recall rate of book text data extraction, the management methods and query efficiency of book information.

Internet Protocol version 6 (IPv6) is envisioned as the cornerstone for future internet connectivity and information technology (IT) expansion. Due to its enormous address pool, extendable headers, high level of security, and mobility, IPv6 is positioned as the next-generation Internet Protocol. NDP is an integral component of IPv6 since it resolves addresses, locates routers, and finds duplicated addresses in a local-link network. Because NDP is based on the premise that all nodes in the network are trustworthy, it is subject to a variety of attacks, including Denial of Service (DoS) on Duplicate Address Detection (DAD) attacks (aka. DoS-on-DAD), Address Resolution-based attacks, Router Advertisement (RA) based attacks, and Redirect attacks. This paper proposes an NDP security (NDPsec) mechanism based on the Ed25519 digital signature to authenticate IPv6 hosts to prevent unauthorized devices from joining the network. The proposed NDPsec mechanism is evaluated and compared to Secure NDP (SeND), Match-Prevention, and Trust-ND mechanisms. The performance is measured in terms of processing time, traffic overhead, and resilience against network-based attacks. The results obtained from the experiments showed that NDPsec successfully prevented cyberattacks, with approximately 144% less processing time and over 50% less traffic overhead compared to SeND (the default security mechanism for NDP protocol). The proposed NDPsec mechanism has remarkable superiority in terms of resilience against attacks compared to Match-Prevention and Trust-ND mechanisms.

In multi-client functional encryption (MC-FE) for predicate queries, clients generate ciphertexts of plaintexts x1,…,xn binding with a time period T and store them on a cloud server, and the cloud server receives a function key corresponding to a predicate f from a trusted center and learns whether f(x1,…,xn)=1 or not by running the decryption algorithm on the multiple ciphertexts of the same time period. MC-FE for predicates can be used for a network event or medical data monitoring system based on time series data gathered by multiple clients. In this paper, we propose efficient MC-FE schemes that support conjunctive equality or range queries on encrypted data in the multi-client settings. First, we propose an efficient multi-client hidden vector encryption (MC-HVE) scheme in bilinear groups and prove the selective security with static corruptions. Our MC-HVE scheme is very efficient since a function key is composed of four group elements, a ciphertext consists of O(ℓ) group elements where ℓ is the size of a plaintext, and the decryption algorithm only requires four pairing operations. Second, we propose an efficient multi-client range query encryption (MC-RQE) scheme and prove the selective weak security with static corruptions. Our MC-RQE scheme which uses a binary tree is efficient since a ciphertext consists of O(logD) group elements and a function key consists of O(nlogD) group elements where D is the maximum value of a range.

Data deduplication is a technique to eliminate duplicate data in order to save storage space and enlarge upload bandwidth, which has been applied by cloud storage systems. However, a cloud storage provider (CSP) may tamper user data or cheat users to pay unused storage for duplicate data that are only stored once. Although previous solutions adopt message-locked encryption along with Proof of Retrievability (PoR) to check the integrity of deduplicated encrypted data, they ignore proving the correctness of duplication check during data upload and require the same file to be derived into same verification tags, which suffers from brute-force attacks and restricts users from flexibly creating their own individual verification tags. In this paper, we propose a verifiable deduplication scheme called VeriDedup to address the above problems. It can guarantee the correctness of duplication check and support flexible tag generation for integrity check over encrypted data deduplication in an integrative way. Concretely, we propose a novel Tag-flexible Deduplication-supported Integrity Check Protocol (TDICP) based on Private Information Retrieval (PIR) by introducing a novel verification tag called note set , which allows multiple users holding the same file to generate their individual verification tags and still supports tag deduplication at the CSP. Furthermore, we make the first attempt to guarantee the correctness of data duplication check by introducing a novel User Determined Duplication Check Protocol (UDDCP) based on Private Set Intersection (PSI), which can resist a CSP from providing a fake duplication check result to users. Security analysis shows the correctness and soundness of our scheme. Simulation studies based on real data show the efficacy and efficiency of our proposed scheme and its significant advantages over prior arts.

Secret key generation (SKG) is a fundamental signal processing problem for security applications including wireless network security and biometric template security. For SKG, a pair (or more) of highly correlated secret vectors (SVs) need to be respectively quantized into a pair (or more) of almost identical sequences of binary bits (i.e., keys). The literatures on SKG in the wireless community almost uniformly default on a direct quantization on SVs. On the other hand, many works on biometric template security advocate a quantization on the output of a one-way function of SVs. In this paper, we present a generalized approach for SKG called continuous encryption before quantization (CEbQ). By CEbQ, a pair of SVs of limited dimension are first transformed by a continuous encryption function into a pair of sequences of quasi-continuous pseudorandom numbers (QCPRNs) of any desired length, and then these QCPRNs are quantized into keys. We show that CEbQ can yield a much lower key error rate than direct quantization subject to standardized randomness tests. Comparisons with other methods are also provided.

Advances in integrated circuit and wireless communication technologies are making wireless body area networks (WBANs) an increasingly important medical paradigm. By collecting, uploading, and processing real-time physical parameters, WBANs assist clients in better recognizing and managing their bodies. Besides conveniences it brings, WBANs are facing the risk of clients’ privacy leakage during data transmission. Anonymous authentication schemes were proposed to resolve this challenge, and latest schemes ensure that even if a WBAN client’s private key is exposed, previous session keys generated by this client cannot be compromised (known as forward security). Unfortunately, previous forward secure schemes need bilinear pairing operations, which is undesirable in computation-resource-bounded WBANs. Furthermore, the property, that once a WBAN client’s private key is exposed, previous sessions shouldn’t be identified (dubbed forward anonymity), hasn’t been considered in existing works. In response to the above challenges, in this paper, we propose an identity-based authenticated encryption method without pairing, and based on this method, we construct an anonymous authentication scheme. Subsequent security and performance analyses demonstrate that our schemes are secure (including forward anonymous) under the random oracle model, and practical in WBANs with limited resources.

The application of data deduplication technology reduces the demand for data storage and improves resource utilization. Compared with limited storage capacity and computing capacity of a single node, cluster data deduplication technology has great advantages. However, the cluster data duplication technology also brings new issues on deduplication rate reduction and load balancing of storage nodes. The application of data routing strategy can well balance the problem of deduplication rate and load balancing. Therefore, this paper proposes a data routing strategy based on distributed Bloom Filter. 1)Superchunk is used as the basic unit of data routing to improve system throughput. According to Broder’s theorem, k leastsized fingerprints are selected as the Superchunk features and send to the storage node. The optimal node is selected as the routing node by matching the BloomFilter, and the storage capacity of the node and maintained in the memory of the storage node. 2) Design and implement system prototypes. The specific parameters of all kinds of routing strategies are obtained through experiments, and the routing strategies proposed in this paper are tested. The theoretical analysis and experimental results prove the feasibility of the strategies proposed by this paper. Compared with the other routing strategies, our method improved 3% of the deduplication rate, reduces the communication query overhead by more than 36% and improves the load balancing degree of the storage system.

In the current digital era, personal data storage on public platforms is a major cause of concern with severe security and privacy ramifications. This is true especially in e-health data management since patient’s health data must be managed following a slew of established standards. The Cloud Service Providers (CSPs) primarily provide computing and storage resources. However, data security in the cloud is still a major concern. In several instances, Blockchain technology rescues the CSPs by providing the robust security to the underlying data by encrypting data using the unique and secret keys. Each network user in Blockchain has its own unique and secret keys linked directly to the transaction keys as a digital signature to protect the data. However, Blockchain technology suffers from the latency and throughput issues in high workload scenarios. To overcome e-healthcare records privacy issues in a third-party cloud, we designed a Patient’s E-Healthcare Records Management System (PRMS) that focuses on latency and throughput. A comprehensive performance analysis of PRMS is carried out on different third-party clouds to validate its applicability. Moreover, the proposed PRMS system is compared with Blockchain platforms such as Hyperledger Fabric v0.6 and Etherium 1.5.8 against latency and throughput by adjusting the workload for each platform up to 10,000 transactions per second. The proposed PRMS is compared to the Secure and Robust Healthcare-Based Blockchain (SRHB) approach using Yahoo Cloud Serving Benchmark (YCSB) and small bank datasets. The experimental results indicate that deploying PRMS on Amazon Web Services decreases System Execution Time (SET) and the Average Delay (AD) time by 2.4%, 8.33%, and 25.15%, 15.26%, respectively. Additionally, deploying PRMS on the Google Cloud Platform decreases System Execution Time (SET) and Average Delay (AD) by 2.27%, 2.4%, and 2.72%, 4.73% AD, respectively. The experimental results confirm the superiority of the PRMS und...

Can software-based packet filters effectively dampen volumetric distributed denial-of-service (DDoS) streams in an era when 10 Gbps links are considered slow? The potential of longest prefix matching (LPM) for enforcing precise DDoS scrubbing policies seems to be overlooked in contemporary packet filtering datapaths, and in this paper, we argue that this should not be the case by showing that effective whitelist / blacklist LPM-based filtering can be performed with commodity hardware. A showcase datapath we propose can evaluate multiple queries in large separate LPM databases for each forwarded 64-byte packet, while sustaining 10 Gbps line rate on a single CPU core, with a healthy scaling potential due to its lockless architecture and small memory footprint of LPM structures. We demonstrated forwarding 64 million packets per second using only six CPU cores while performing independent lookups for each packet in three large LPM databases created by aggregating malicious IP addresses or by mapping different geolocation identifiers to IPv4 prefixes.

This paper examines data injection attacks on distributed statistical estimation. We consider a dynamically changing distributed network consisting of N agents exchanging information over time. The N agents share the common goal of minimizing a joint objective function, which is the average of the private objective functions in a distributed manner. The private objective function is a realization of an objective function known to all the agents, but uses private data known to the agent alone. The agents’ data are independent and identically distributed. We have previously proposed a novel data injection attack on the Distributed Projected Gradient (DPG) algorithm which is performed locally by malicious nodes in the network that steer the network’s final state to a state of their choice. The proposed attack cannot be detected using previously described techniques. We propose a new detection and localization scheme, performed in a single instance unlike other methods that require the algorithm to run for many instances to acquire statistics over time. This detection and localization scheme is performed by each agent and is purely local, and does not involve decisions made by other agents. Whenever an agent suspects another agent to be an attacker, it will block its data, and maintain convergence to the true optimal state. We provide exponential bounds for the probability of false alarm and probability of attacker detection and localization. Simulations show that when all the attackers are detected and isolated by each agent, the network will recover and converge to the true optimal state.

The active growth of Internet-based applications such as social networks and e-commerce websites leads people to generate a tremendous amount of opinions and reviews about products and services. Thus, it becomes very crucial to automatically process them. Over the last ten years, many systems have been proposed to generate and visualize reputation by mining textual and numerical reviews. However, they have neglected the fact that online reviews could be posted by malicious users that intend to affect the reputation of the target product. Besides, these systems provide an overall reputation value toward the entity and disregard generating reputation scores toward each aspect of the product. Therefore, we developed a system that incorporates spam filtering, review popularity, review posting time, and aspect-based sentiment analysis to generate accurate and reliable reputation values. The proposed model computes numerical reputation values for an entity and its aspects based on opinions collected from various platforms. Our proposed system also offers an advanced visualization tool that displays detailed information about its output. Experiment results conducted on multiple datasets collected from various platforms (Twitter, Facebook, Amazon … ) show the efficacy of the proposed system compared with state-of-the-art reputation generation systems.

The large expansion in network services and applications seen in the last few years requires new network architectures to satisfy an increasing number of users and enhance content delivery. Named Data Networking (NDN) has recently appeared as a new paradigm to solve many shortcomings in the current TCP/IP architecture. Its main characteristics like stateful forwarding and in-network caching made NDN networks an efficient environment for data delivery where the data is retrieved based on content names rather than IP addresses. The NDN, by its nature, defends against the well-known Distributed Denial of Service (DDoS) attacks that take place in the traditional TCP/IP architecture. However, a special kind of DDoS attack called Collusive Interest Flooding Attack (CIFA) has appeared to overwhelm the resources of NDN routers by filling their Pending Interest Tables (PIT) with long-lasting malicious entries. The network throughput and consumer satisfaction rate are highly affected by CIFA. A lightweight yet efficient stateless CIFA detection algorithm is proposed in this research utilizing the non-parametric CUSUM algorithm; a change point detection approach that detects the point in time when a transition occurs in the network. The proposed algorithm is characterized by its low computational overhead, highly accurate detection, and quick response. To detect the malicious name prefixes and eliminate the CIFA effect, a mitigation algorithm that uses the average response time vales of all name prefixes is proposed in this research. Experimental results show that this approach detects CIFA after 199.5 ms from when an attack is launched in the large-scale topology. In addition, the mitigation approach effectively reduces the PIT utilization and increases the average consumer satisfaction rate.

In the current era of information explosion, users’ demand for data storage is increasing, and data on the cloud has become the first choice of users and enterprises. Cloud storage facilitates users to backup and share data, effectively reducing users’ storage expenses. As the duplicate data of different users are stored multiple times, leading to a sudden decrease in storage utilization of cloud servers. Data stored in plaintext form can directly remove duplicate data, while cloud servers are semi-trusted and usually need to store data after encryption to protect user privacy. In this paper, we focus on how to achieve secure de-duplication and recover data in cipher text for different users, and determine whether the indexes of public key searchable encryption and the matching relationship of trapdoor are equal in cipher text to achieve secure de-duplication. For the duplicate file, the data user’s re-encryption key about the file is appended to the cipher text chain table of the stored copy. The cloud server uses the re-encryption key to generate the specified transformed cipher text, and the data user decrypts the transformed cipher text by its private key to recover the file. The proposed scheme is secure and efficient through security analysis and experimental simulation analysis.

This study addresses the issue of secure control design for cyber-physical systems (CPS) against denial of service (DoS) attacks. We take into account a continuous-time linear system with a convex quadratic performance measure and a sampled linear state feedback control. DoS attacks impose constraints on the CPS, where packets may be jammed between the sensor and controller by a malicious entity, potentially resulting in system instability and performance degradation. We assume that the attacker can perform DoS attacks with a limited time and frequency due to energy restrictions. We devise an efficient procedure using the linear matrix inequality approach to compute an upper bound on the performance degradation brought on by the DoS attack. We also propose a redesign of the controller to minimize this performance degradation. Finally, a simulation example illustrates the computation of the performance degradation under a bounded DoS attack and the design of a secure controller. Simulation results show that the designed controller effectively keeps the feedback loop’s performance and stability under attack.

In recent years, the epidemic of communicable diseases has boosted the prevalence of online teaching activities. But how to make smart evaluation towards teaching effect has always been a technical barrier. As consequence, this paper utilizes fuzzy comprehensive analysis to deal with this problem from the perspective of big data mining. In particular, it proposes a data-driven smart evaluation framework for teaching effect based on fuzzy comprehensive analysis. Firstly, business data is timely collected from online courses as the basis, including teacher performance, teaching contents, student feedback, etc. Specifically, the initial data is encoded into structured format, from which characteristics of students behaviors can be analyzed. Then, the fuzzy comprehensive analysis is utilized to calculate evaluation results of teaching effect. Some simulation experiments are conducted based on the computer programming design, in which the proposal technical framework is implemented on a developed Web platform. The experiments reflect that the proposal can well realize evaluation of teaching effect.

Fog computing has emerged as a computing paradigm for resource-restricted Internet of things (IoT) devices to support time-sensitive and computationally intensive applications. Offloading can be utilized to transfer resource-intensive tasks from resource-limited end devices to a resource-rich fog Or cloud layer to reduce end-to-end latency and enhance the performance of the system. However, this advantage is still challenging to achieve in systems with a high request rate because it leads to long queues of tasks in fog nodes and reveals inefficiencies in terms of delays. In this regard, reinforcement learning (RL) is a well-known method for addressing such decision-making issues. However, in large-scale wireless networks, both action and state spaces are complex and extremely extensive. Consequently, reinforcement learning techniques may not be able to identify an efficient strategy within an acceptable time frame. Hence, deep reinforcement learning (DRL) was developed to integrate RL and deep learning (DL) to address this problem. This paper presents a systematic analysis of using RL or DRL algorithms to address offloading related issues in fog computing. First, the taxonomy of fog computing offloading mechanisms based on RL and DRL algorithms was divided into three major categories: value-based, policy-based, and hybrid based algorithms. These categories were then compared based on important features, including offloading problem formulation, utilized techniques, performance metrics, evaluation tools, case studies, their strengths And drawbacks, offloading directions, offloading mode, SDN-based architecture, and offloading decisions. Finally, the future research directions and open issues are discussed thoroughly.

The configuration of security functions in computer networks is still typically performed manually, which likely leads to security breaches and long re-configuration times. This problem is exacerbated for modern networks based on network virtualization, because their complexity and dynamics make a correct manual configuration practically unfeasible. This article focuses on packet filters, i.e., the most common firewall technology used in computer networks, and it proposes a new methodology to automatically define the allocation scheme and configuration of packet filters in the logical topology of a virtual network. The proposed method is based on solving a carefully designed partial weighted Maximum Satisfiability Modulo Theories problem by means of a state-of-the-art solver. This approach formally guarantees the correctness of the solution, i.e., that all security requirements are satisfied, and it minimizes the number of needed firewalls and firewall rules. This methodology is extensively evaluated using different metrics and tests on both synthetic and real use cases, and compared to the state-of-the-art solutions, showing its superiority.

While users enjoy the convenience of data outsourcing in the cloud, they also face the risks of data modification and private information leakage. Searchable encryption technology can perform keyword searches over encrypted data while protecting their privacy and guaranteeing the integrity of the data by verifying the search results. However, some associated problems are still encountered, such as the low efficiency of verification and uncontrollable query results. Accordingly, this paper proposes a Privacy-Preserving Searchable Encryption (PPSE) scheme based on public and private blockchains. First, we store an encrypted index in a private blockchain while outsourcing corresponding encrypted documents to a public blockchain. The encrypted documents are located through the encrypted index. This method can reduce the storage overhead on the blockchains, and improve the efficiency of transaction execution and the security of stored data. Moreover, we adopt a smart contract to introduce a secondary verification access control mechanism and restrict data users' access to the private blockchain through authorization for the purpose of guaranteeing data privacy and the correctness of access control verification. Finally, the security analysis and experimental results indicate that compared with existing schemes, the proposed scheme can not only improve the security of encrypted data but also guarantee the efficiency of the query.

To protect data privacy, users prefer to store encrypted data in cloud servers. Cloud servers reduce the cost of storage and network bandwidth by eliminating duplicate copies. To address the potential internal data leakage problem, the concept of clustering deviation is proposed for the first time. We improve the DBSCAN algorithm to tolerate clustering deviation. A data deduplication scheme is built upon the new algorithm, which considers users as clustering samples. Instead of immediately re-clustering new users, a certain deviation is tolerated to assign the users to the existing classes. We determine the popularity of the data according to user clustering results and apply different encryption schemes to protect the security of unpopular data more effectively. The performance of the algorithm is analyzed and compared with other methods through experiments, and the results verify the feasibility and efficiency of the proposed deduplication scheme.

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network edge. However, current computation offloading studies in the heterogeneous edge environment face multifaceted challenges: Dependencies among computational tasks, resource competition among multiple users, and diverse long-term objectives. Mobile applications typically consist of several functionalities, and one huge category of the applications can be viewed as a series of sequential tasks. In this study, we first proposed a novel multiuser computation offloading framework for long-term sequential tasks. Then, we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading problem. Moreover, we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve them. We further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

Cloud storage is an essential method for data storage. Verifying the integrity of data in the cloud is critical for the client. Traditional cloud storage approaches rely on third-party auditors (TPAs) to accomplish auditing tasks. However, third-party auditors are often not trusted. To eliminate over-reliance on third-party auditors, this paper designs a block chain-based auditing scheme that uses block chain instead of third-party auditors to ensure the reliability of data auditing. Meanwhile, our scheme is based on the audit method of the quad Merkle hash tree, using the root of the quad Merkle hash tree to verify the integrity of data, which significantly improves computing and storage efficiency. Automated verification of auditing activities by deploying smart contracts on the block chain allows us to have a more up-to-date picture of data integrity. The performance of the scheme is evaluated through security analysis and experiments, which prove that the proposed scheme is secure and effective.

Storing and sharing health records through electronic systems pose security risks. To address them, several countries’ regulations have established that healthcare information systems must fulfill security properties (confidentiality, access control, integrity, revocation and anonymity) and complementary ones (emergency access and interoperability). Upon tackling these issues, several proposals present security limitations and/or address specific properties only. We propose Sec-Health, a block chain-based protocol that secures health records, addressing all of the main security and complementary properties defined in current regulations. We show that Sec-Health is a suitable solution by analyzing it under several attack scenarios and describing how it overcomes the problems of existing solutions.

Block chains are a new approach to creating distributed networks that were first introduced in 2008. It allows the formation of peer-to-peer networks based on consensus, forming chains from accepted Blocks without requiring a central authority or centralized controller. A prominent application of this Technology is its use in decentralized storage systems. Individuals in decentralized storage networks rent unused hardware storage space to other individuals. A decentralized network utilizing end-to-end encryption eliminates the risk of data loss associated with centralized data control by enabling clients to transmit their files securely. The storage providers must prove that they have kept unaltered files in this network for this time. Many studies have been conducted in this specific domain, most targeting storage capacity and efficiency, but a security, integrity and privacy loophole need to be addressed. This paper presents an overview of blockchain-based storage systems and how they work, followed by a comparison with cloud-based storage networks and a survey of various decentralized storage networks like SIA, File coin, and Storj available on the market. Next, we discuss the advantages and disadvantages of blockchain-based storage. In our final discussion, we examine the security problems of decentralized storage networks and explore potential solutions and research directions for the future.

As for the advancement of IoT and cloud computing in healthcare, outsourcing encrypted Electronic medical records (EMRs) created by the aggregation of medical treatment applications and health data collected from IoT devices enables high accessibility, effective collaboration, and zero computational operation cost. Current applications and research works generally concern the privacy of the finest EMRs that are encrypted with secure and lightweight cryptographic protocols before they are outsourced to the cloud. However, this process does not consider the security and privacy of the data collected by IoT devices, where the data being transferred can be leaked before they are aggregated. Furthermore, existing IoT-cloud based access control solutions have not addressed the outsourced encryption, privacy of IoT data transmission and aggregation, and the policy update of the EMRs in an integrated manner. In this paper, we propose an access control scheme called Lighted which provides secure, fine-grained, and scalable EMR sharing in a cloud-based environment integrated with fog computing, CP-ABE, and blockchain technology. We propose a secure IoT data transmission and aggregation method based on lightweight encryption and digital signing. At the core, we introduce outsourced encryption with a privacy-preserving access policy scheme and an outsourced encryption and decryption algorithm leveraged by the collaboration between fog nodes and blockchain. In addition, we introduce a novel lightweight policy update algorithm to enable the data owners of EMRs to effectively manage their policies in a secure and effective manner. Finally, we performed the comparative analysis to illustrate the computation cost and conducted experiments to evaluate the performance of our scheme and related works. The experimental results showed that our scheme outperformed existing works since it yielded least processing cost of both encryption and decryption at end-users’ devices, which demonstrates the higher efficiency and practicality of our scheme.

In recent years it has been shown that the secure exchange of medical information significantly benefits people’s life quality, improving their care and treatment. The interoperability of the entire healthcare ecosystem is a constant challenge, and even more, with all the risks posed to the security of healthcare information. Blockchain technology is emerging as one of the main alternatives when it comes to finding a balance in the healthcare ecosystem. However, the constant development of new Blockchain technologies and the evolution of healthcare systems make it difficult to find established proposals. From an architectural point of view, the design of blockchain-based solutions requires trade-offs e.g., security and interoperability. This paper focuses on two main objectives, in the first one, it was carried out a Systematic Literature Review for exploring architectural mechanisms used to support the interoperability and security of Blockchain-based Health Management Systems. Taking into account of results, a series of scenarios were generated where these mechanisms can be used along with their context, issues, and various architectural concerns (interoperability and security). In the second objective, a high-level architecture and its validation were proposed through an experiment for the whole process of developing a Domain Specific Language, using the Model Driven Engineering methodology for specific Smart Contracts.

Continuity of care requires the exchange of health information among organizations and care teams. The EU General Data Protection Regulation (GDPR) establishes that subject of care should give explicit consent to the treatment of her personal data, and organizations must obey the individual’s will. Nevertheless, few solutions focus on guaranteeing the proper execution of consents. We propose a service-oriented architecture, backed by blockchain technology, that enables: (1) tamper-proof and immutable storage of subject of care consents; (2) a fine-grained access control for protecting health data according to consents; and (3) auditing tasks for supervisory authorities (or subjects of care themselves) to assess that healthcare organizations comply with GDPR and granted consents. Standards for health information exchange and access control are adopted to guarantee interoperability. Access control events and the subject of care consents are maintained on a blockchain, providing a trusted collaboration between organizations, supervisory authorities, and individuals. A prototype of the architecture has been implemented as a proof of concept to evaluate the performance of critical components. The application of subject of care consent to control the treatment of personal health data in federated and distributed environments is a pressing concern. The experimental results show that blockchain can effectively support sharing consent and audit events among healthcare organizations, supervisory authorities, and individuals.

The Internet of Things (IoT) is vulnerable to leakage of private information during data sharing. To avoid this problem, access control and secure data sharing have been introduced in IoT; however, many challenges are faced because of centralized access control and single delegator selection. Additionally, blockchain is integrated into IoT to enhance the security of the environment. For that purpose, this research proposes dynamic secure access control using the blockchain (DSA-Block) model, which performs secure access control and data sharing. Initially, the IoT device attributes and user attributes are registered at a local domain authority (LDA) for generating private and public keys using the hyperelliptic curve cryptography (HECC) algorithm, which ensures the legitimacy of the users and devices. Then, the IoT devices send a request message to the edge nodes (ENs) via a gateway, which performs request filtration by validating the user’s authenticity. The filtered requests are sent to the edge server to perform access delegation using rock hyraxes swarm optimization (RHSO), which selects a set of delegator nodes. The access control decision is made by using the trusted practical Byzantine fault tolerance (PBFT) consensus algorithm. The IoT data are stored in the cloud server for secure storage, in which the data are secured using a differential privacy mechanism. Finally, dual revocations, such as user attribute revocation and user revocation, are used to maintain security. The performance of DSA-Block is evaluated and the results demonstrate that the proposed DSA-Block model achieves superior performance compared to previous works.

Despite the maturity of cloud services (e.g., outsourcing of computational tasks), a number of operational challenges remain. For example, how do we ensure trust between outsourcers and workers in a zero-trust environment? While a number of blockchain-based solutions that eliminate the reliance on trusted third parties have been presented, many of these existing approaches do not achieve robust fairness and/or support compatibility with other systems. In this paper, we propose an efficient fair payment system using blockchain (EFPB), designed to achieve robust fairness and compatibility. Specifically, EFPB comprises a number of cryptographic building blocks, mainly: one-way accumulator (RSA-based construction), stealth address and symmetric encryption. We then evaluate the performance of EFPB to demonstrate that it is more efficient and low-cost than other competing schemes, as well as presenting a comparative summary of functionalities.

Given the challenge of manufacturing data silos and information credibility issues in traditional aviation suppliers’ result-oriented management approach, this paper proposes a block chain-based aviation supplier manufacturing process quality data-sharing platform. Firstly, the paper introduces the possibility of integrating manufacturing supply chain quality management with block chain technology. Secondly, the quality and data sharing platform architecture of the production process of new aviation suppliers is presented based on quality state and island kinds of aviation suppliers. Then, a detailed method for the implementation of quality and data security sharing is proposed to support the sharing platform’s real-time and orderly operation. Build critical technologies such as manufacturing quality data block packaging models, data storage security sharing, and supplier assessment models on this foundation. Finally, depending on data collection of supplier product production processes to shared application practices based on a specific aircraft industrial park under the supervision of the platform architecture and technology. The application platform integrates the data supply and request components, providing practical and intelligent sharing solutions for airlines’ product quality data.

Cardiovascular disease is the primary reason for mortality worldwide, responsible for around a third of all deaths. To assist medical professionals in quickly identifying and diagnosing patients, numerous machine learning and data mining techniques are utilized to predict the disease. Many researchers have developed various models to boost the efficiency of these predictions. Feature selection and extraction techniques are utilized to remove unnecessary features from the dataset, thereby reducing computation time and increasing the efficiency of the models. In this study, we introduce a new ensemble Quine McCluskey Binary Classifier (QMBC) technique for identifying patients diagnosed with some form of heart disease and those who are not diagnosed. The QMBC model utilizes an ensemble of seven models, including logistic regression, decision tree, random forest, K-nearest neighbour, naive bayes, support vector machine, and multilayer perceptron, and performs exceptionally well on binary class datasets. We employ feature selection and feature extraction techniques to accelerate the prediction process. We utilize Chi-Square and ANOVA approaches to identify the top 10 features and create a subset of the dataset. We then apply PrincipalComponentAnalysistothesubsettoidentify9primecomponents.Weutilizeanensembleofallsevenmodels and the Quine McCluskey technique to obtain the Minimum Boolean expression for the target feature. The results of the seven models are considered independent features, while the target attribute is dependent. We combine the projected outcomes of the seven ML models and the target feature to form a foaming dataset. We apply the ensemble model to the dataset, utilizing the Quine McCluskey minimum Boolean equation built with an 80:20 train-to-test ratio. Our proposed QMBC model surpasses all current state-of-the-art models and previously suggested methods put forward by various researchers.

Open ID Connect (OIDC) is one of the most widely used delegated authentication protocols in webandmobile applications providing a single sign-on experience. It allows third-party applications, called Relying Parties (RP), to securely request and receive information about authenticated sessions and end-users from an identity provider. The OIDC specification defines several parameters, including the client_id, client secret, authorization code, access token, id token, state, and redirect_uri, as keys to the protocol operation, with significant security and privacy implications. Therefore, securing these parameters is critical to prevent attackers from impersonating legitimate entities, gaining unauthorized access, having complete control over users’ accounts, and/or violating their privacy. To enhance OIDC security and preserve its users’ privacy, we propose a novel model for OIDC based on the Ethereum Blockchain and the non-fungible token (ERC721) standard. To prove the robustness and safety of the proposed system, we perform a detailed security analysis formally using the most widely accepted protocols security verification tools, AVISPA and Scyther, and informally by discussing various attacks. The analysis results show that the proposed system is resilient against well-known attacks. Furthermore, we evaluate the cost and performance of the proposed solution, confirming its affordability and assuring that our approach does not impact the user experience and performance of existing OIDC-based systems. Finally, we conduct a security and privacy comparative analysis with similar existing systems, proving the superiority and efficiency of our proposed Blockchain based OIDC system.

Data security and integrity are becoming increasingly important as the volume of data being created and stored grows. A controlled third party that provides most of the existing big data security systems makes them susceptible to several security risks. By resolving current technology challenges, including scalability, non-tampering, trustworthiness, data governance, and transparency, block chain technology plays a vital role and has a significant potential to safeguard personal information. Therefore, this work focuses on addressing real-time big data storage issues based on a transdisciplinary research approach. This study introduces a brand-new approach to big data storage security that leverages block chain technology and applies highway protocol to generate new blocks. The proposed highway protocol works based on the flexible finality condition to overcome issues of baseline models. The highway allows blocks to run the consensus mechanism to configure security thresholds more freely. The proposed protocol also allows blocks with lower thresholds to reach finality more quickly than blocks requiring greater degrees of confidence. Therefore, the proposed big data framework can dynamically control data manipulation and continuously support individuals to participate in the data-sharing process. A comparison was performed with the number of data requests in terms of hit ratio, and a highway protocol provides better results than baseline models. The proposed model provides a data processing period of 13 to 30 ms and an energy consumption of 32 to 41 mJ.

With the proliferation of privacy issues surrounding the Internet of Medical (IoMT) recommender system data, this study presents a Secure Recommendation and Training Technique (SERTT) which is contingent on a combination of both federated learning and blockchain approaches. Firstly, the study presents a new framework for recording, sorting, and transmission of IoMT data while incorporating blockchain to ensure that the IoMT data transmitted to cloud servers is not made vulnerable by the sharing of the original data. Secondly, by utilizing medical data, the study designs a Recommender Data Management Neural Architecture (REDMANA) which is based on federated learning and model searching training framework. The proposed technique guarantees that the model gradients which are trained by each node are not disclosed all through the universal training and modeling procedure. This makes the raw data inaccessible to either the IoMT data provider or the user. Considering that the model ensures that users can only obtain their necessary inquiries, neither medical data suppliers nor users can obtain access to raw data. Thus, it reduces the issues of safeguarding medical data sets to the issues of securing data processing. Using numerical analysis and experiments the proposed technique is compared with other existing techniques, the result shows that the proposed SERTT system is efficient and secures recommender data management training and modeling technique and that it performs previously designed techniques as compared.

Currently, many companies and institutions use centralized or distributed databases to store massive amounts of data. However, the use of untrusted centralized third-party auditors can result in security issues because these auditors may be malicious and tamper with or delete user data. This poses a significant challenge for ensuring the reliability of the data verification results. Although introducing a third-party auditor can help address this issue, it may also be untrustworthy and collude with the database service provider to forge false data verification results. In this study, we propose a data integrity verification scheme using smart contracts (DIV-SC) to address this challenge in a centralized database environment. Our approach utilizes block chain technology as a decentralized third-party auditor, ensuring that the information stored on the blockchain is immutable and cannot be tampered with maliciously. In addition, smart contracts deployed on the blockchain can ensure that the verification procedures are correct and are not affected by any malicious party.We also leverage game theory to improve the reliability of multiple verifications, reduce verification time and improve overall performance. Our proposed scheme reduces the total verification time consumption by up to 53.44% while increasing the number of verifiable times by nearly 3.8 times, compared to conventional data integrity verification schemes.

Secure retrieval of remote sensing images in an outsourced cloud environment garners considerable attention. Since the cloud service provider (CSP) is considered as a semitrusted third party that may return incorrect retrieval results to save computational resources or defraud retrieval fees for profit, it becomes a critical challenge to achieve secure and verifiable remote sensing image retrieval. This article presents a secure retrieval and blockchain-assisted verifiable scheme for encrypted remote sensing images in the cloud environment. In response to the characteristic that geographical objects in remote sensing images with clear category attributes, we design a remote sensing image retrieval method to facilitate secure and efficient retrieval. In addition, we propose a verifiable method combined with blockchain and Merkle trees for checking the integrity and correctness of the storage and retrieval services provided by CSP, which can replace the traditional third-party auditor. The security analysis and experimental evaluation demonstrate the security, verifiability, and feasibility of the proposed scheme, achieving secure remote sensing image retrieval while preventing malicious behavior of CSP.

Online social networks (OSNs) are becoming more and more popular in people's lives as the demand for online interaction continues to grow. Current OSNs are using centralized identity management system (IDM), which has some problems of single point of failure and privacy data leakage. The emergence of decentralized identity (DID) can solve these problems. However, most existing DID systems have some privacy issues that a user's attributes value are disclosed while accessing service. In this paper, we design a DID protocol to solve these challenges. The proposed protocol includes a range proof protocol to provide attribute privacy. The range proof protocol works with anonymous credentials and does not need a trusted setup. Moreover, the identity model behind the DID protocol is extended from an existing model, which achieves identity revocation. Finally, we implement a system prototype on the blockchain for evaluation. The security analysis shows that our protocol provides stronger privacy protection. The performance evaluation indicates that the computation cost and blockchain overheads of our protocol are acceptable in OSNs.

With the emergence of new information technology fields and various usages of internet applications, securing massive amounts and multiple varieties of multimedia data has become a significant challenge. Hence, it is essential to investigate and develop new cryptographic algorithms to ensure the protection and privacy of multimedia data at rest and during transmission through the communication channel while attempting to overcome the limitations of traditional cryptographic methods. This paper suggests a novel image encryption and decryption technique based on a computational genetic approach and self-adaptive chaotic substitution. The proposed encryption method depends mainly on four steps: sequence generation, diffusion, confusion, and optimization. The USC-SIPI image data is being utilized to prove the correctness and effectiveness of our approach in defending against different attack types. Furthermore, the proposed technique could be capable of withstanding attacks while achieving an information entropy of 7. 999, Number of Pixel Change Rate of 99.62%, and a Unified Average Change in Intensity of 33.54%, respectively. The results of security testing and analysis revealed that our image security method is strongly recommended for modern communications security applications.

Reversible data hiding in encrypted images (RDHEI) is an essential data security technique. Most RDHEI methods cannot perform well in embedding capacity and security. To address this issue, we propose a new RDHEI method using Chinese remainder theorem-based secret sharing (CRTSS) and hybrid coding. Specifically, a hybrid coding is first proposed for RDH to achieve high embedding capacity. At the content owner side, a novel iterative encryption is designed to conduct block based encryption for perfectly preserving the spatial correlation of original blocks in their encrypted blocks. Then, the CRTSS with the constraints is exploited to generate multiple encrypted image shares, in which spatial correlations of the encrypted blocks are also preserved. Meanwhile, the CRTSS provides good security properties for the proposed method. Since there are strong spatial correlations in the blocks of each share, the data-hider can exploit the proposed hybrid coding to perform data embedding for improving capacity. On the receiver side, even if some shares are corrupted/missing, the original image can be losslessly recovered as long as enough uncorrupted marked shares are obtained. Experiment results show that the proposed RDHEI method outperforms some state-of-the-art methods, including some secret sharing (SS) based methods in terms of embedding capacity.

Compared with cloud computing environment, edge computing has many choices of service providers due to different deployment environments. The flexibility of edge computing makes the environment more complex. The current edge computing architecture has the problems of scattered computing resources and limited resources of single computing node. When the edge node carries too many task requests, the make span of the task will be delayed. We propose a load balancing algorithm based on weighted bipartite graph for edge computing (LBA-EC), which makes full use of network edge resources, reduces user delay, and improves user service experience. The algorithm is divided into two phases for task scheduling. In the first phase, the tasks are matched to different edge servers. In the second phase, the tasks are optimally allocated to different containers in the edge server to execute according to the two indicators of energy consumption and completion time. The simulations and experimental results show that our algorithm can effectively map all tasks to available resources with a shorter completion time.

Cloud computing is a promising platform for running massive workflow applications based on a pay-per-use model. In cloud computing, the reduction of energy consumption and providing security to workflow scheduling are the key research areas. The primary focus of the existing algorithms, viz., particle swarm optimization (PSO), crow Search optimization (CSO) and other non-metaheuristic algorithms like Round Robbin (RR), SJF, Min-Min, Min-Max etc., is based on the execution time and cost of the workflow applications as a budget constraint. However, these algorithms failed to adequately determine energy consumption, resource utilization, and security in workflow scheduling. To address this issue, a multi-objective scheduling framework is proposed. In this paper, the framework performs dynamic workflow scheduling using universal unique identification- Blake (UUID-Blake), Manhattan Distance-Partition around algorithm (MD-PAM), Linear Scaling-Crow Search Optimization (LS-CSO), Anova-Recurrent Neural Network. The implementation of this framework was achieved in three phases (Phase 1, Phase 2, and Phase 3). Phase 1 is about user registration and authentication using UUID-Blake, which enhances security by allowing legitimate users into the cloud environment. Phase 2 deals with clustering and resource monitoring using MD-PAM and A-RNN, to reduce make span the similar tasks are clustered using task length and maximize the resource utilization by predicting the resource availability. Phase 3 deals with the scheduling of dynamic workflows using LS-CSO by selecting suitable virtual machines. We have considered the heterogeneous computing scheduling problem (HCSP) and grid workload archive (GWA)-T-12 Bit brains datasets for comparing our proposed framework with existing works. Based on the result analysis, the proposed LS-SCO outperformed when compared with the algorithms CSO, PSO and RR has achieved better performance.

After analyzing the long-term evolution (LTE) authentication and key agreement process (EPS-AKA), its existing security vulnerabilities are pointed out. Based on elliptic curve cryptography (ECC) self-certified public keys, this paper proposes an ECC self-certified authentication key agreement scheme (ESC-AKA). This scheme includes the addition of a trusted center (TC), which generates the public keys for the home subscriber server (HSS), the mobility management entity (MME), and the user equipment (UE). Three communication protocols are designed, including MME/HSS registration, UE registration, and UE access. A strand space model is used to carry out the formal analysis, and performance and security analyses are carried out. The results show that this scheme can compensate for the security vulnerabilities of the original EPS-AKA scheme. It implements the encrypted transmission of the international mobile subscriber identity (IMSI), and realizes the mutual authentication between the HSS and MME, the MME and UE, and the HSS and UE. Because the self-certified public key cryptosystem is adopted in this scheme, communication encryption is ensured, and the risk of the TC simultaneously mastering the public and private keys is avoided. This scheme is proven to be effective in protecting the communication security of the LTE network.

Cloud computing technology has undergone tremendous growth in recent years, and there are now many cloud service providers (CSPs). This makes CSP selection a challenging process for cloud users. Further complications arise when users modify the priorities of their requirements. Moreover, concerns such as complex computation, inconsistencies, and rank reversal have been raised in current approaches, resulting in less reliable results. This study presents a new hybrid multiple-criteria decision-making (MCDM) framework for ranking cloud services based on Markov chains combined with the best-only method (BOM). The Markov chain is used to record and track the changes in the priorities of user requirements and determine their final values. Then, the BOM method is utilized to determine the final weights of the QoS criteria based on pairwise comparisons made by the cloud user or decision maker and the final priorities of the user’s requirements. Finally, the cloud services are ranked, and the best CSP is selected. The proposed framework was validated using a case study and a real dataset. Performance, consistency, rank conformance, and sensitivity analyses were performed to evaluate the proposed framework. The obtained results prove that the proposed framework is computationally efficient and fully consistent, considers the user requirements and its transition pattern, and is robust to rank reversal.

At a significant moment in the rapid development of cloud technology, large-scale cloud computing centers have emerged. With the emergence of the internet and artificial intelligence, enormous computing resources are required to process data and train machine learning models. The architecture of cloud computing centers involves millions of computing resources, and improper management of these resources can increase operating costs and exert tremendous pressure on the environment. This study proposes an optimized computing resource and energy management algorithm for computing centers with heterogeneous computing resources from the perspective of Green IT. Specifically, this study models the energy consumption at each point in time and the relationship between tasks and also considers the calculation of data backup. This approach will be expanded to optimize decisions for all computing tasks in computing centers based on the sequence of tasks and energy consumption while considering heterogeneous computing resources, energy efficiency, task scheduling, and execution time. By modeling this issue as a highly nonlinear optimization problem and utilizing mathematical programming and Lagrangian relaxation, we propose an optimized energy management algorithm to effectively manage computing resources and Create cloud computing centers with high performance and low energy consumption.

Cloud computing is a potential platform transforming the health sector by allowing clinicians to monitor patients in real-time using sensor technologies. However, the users tend to transmit sensitive and classified medical data back and forth to cloud service providers for centralized processing and storage. This presents opportunities for hackers to steal data, intercept data in transit, and deprive patients and healthcare providers of private information. Consequently, Security and privacy are the primary concerns that must be addressed for the healthcare organization to trust and adopt the cloud computing platform. We present data sanitization and restoration processes to generate the keys from the acquired data and develop a multi-objective function for the hiding ratio, degree of modification, and information preservation ratio. We then employed the Bee-Foraging Learning-based Particle Swarm Optimization (BFL-PSO) algorithm to acquire the optimal key while transferring healthcare data into the cloud to ensure high Security. The experiment is carried out on the UHDDS dataset. The performance is assessed in terms of Security, delay time, encryption time, error rate, and convergence speed, with the results contrasted to state-of-the-art works. The performance study demonstrates that the suggested algorithm has higher Security than cutting-edge security algorithms.

FOG computing enhances communication efficiency by processing data at the network’s edge. In many critical infrastructure (CI) networks, user entities outsource data to the FOG server, but the reliability of the data is a concern. To avoid incorrect decision-making due to corrupt data, a data auditing process is mandatory. Further, a group of user entities in a CI network generates data onto a common file and outsources the file to the FOG server. Auditing such shared group data creates additional complexities. Existing shared group data auditing schemes using public key infrastructure (PKI) or identity-based cryptography (IBC) face challenges such as certificate management or key escrow problems respectively. While, certificate less cryptography (CLC) offers better security and efficiency, but many CLC-based shared group data auditing schemes lack protection against key generation center compromise. The key generation process in many of the existing shared group data auditing schemes is centralized and does not support flexible user entity admission. This paper proposes a distributed and decentralized cryptography approach to enhance the security of key generation for group members. The scheme offers flexible user admission, allowing for full or semi-membership. The proposed scheme provides efficient and secure shared group data auditing against public key replacement, metadata forgery, and integrity attacks, as shown by performance evaluation and security analysis.

The key exposure is a serious threat for the security of data integrity auditing. Once the user’s private key for auditing is exposed, most of the existing data integrity auditing schemes would inevitably become unable to work. To deal with this problem, we construct a novel and efficient identity-based data integrity auditing scheme with key-exposure resilience for cloud storage. This is achieved by designing a novel key update technique, which is fully compatible with used in identity-based data integrity auditing. In our design, the Third Party Auditor (TPA) is responsible for generating update information. The user can update his private key based on the private key in one previous time period and the update information from the TPA. Furthermore, the proposed scheme supports real lazy update, which greatly improves the efficiency and the feasibility of key update. Meanwhile, the proposed scheme relies on identity-based cryptography, which makes certificate management easy. The security proof and the performance analysis demonstrate that the proposed scheme achieves desirable security and efficiency.

The use of encryption is essential to protect sensitive data, but it often poses challenges when it comes to locating and retrieving information without decryption. Searchable encryption provides an effective mechanism that achieves secure search over encrypted data. In this paper a new approach to address the fine-grained search and to protect sensitive data, Blockchain Assisted ciphertext policy decentralized attribute-based encryption (BA-CP-DABE) in cloud has been developed. The CP-DABE is employed to manage data access, secure key generation, while the immutability of blockchain ensures the confidentiality of ciphertext. By leveraging searchable encryption, it becomes possible to securely search encrypted data stored on the blockchain. Keywords are encrypted using attribute-based encryption and stored on a remote server, along with the corresponding ciphertext in the blockchain. One of the significant challenges in this approach is the assumptions-based technique i.e., bilinear mapping, which involves keyword ciphertext and trapdoor security. However, through extensive numerical experiments, the system has demonstrated its ability to generate key and trapdoor structures, as well as effectively find keywords within the encrypted data.

The convenience and efficient management of cloud servers have resulted in an increasing number of users opting to store their data in the cloud. Consequently, data-outsourcing services relying on cloud servers have been extensively deployed and utilized. In this case, before outsourcing the data to cloud storage, we should ensure unauthorized users cannot access the data. In this article, we present a scheme termed MKSABE-VaAR (multi-keyword searchable attribute-based encryption with verification and attribute revocation). Aiming at the problems of inefficiency, excessive computation in the search process, and only supporting single-keyword search in most attribute-based searchable encryption schemes, first, we package multiple keywords into a polynomial to realize multi-keyword search. Based on this polynomial, MKSABE-VaAR can reduce the amount of search calculation for keyword cipher text and improve search efficiency by reducing the number of bilinear pairing operations. At the same time, we have made some special constructs for indexing to add verification of user attributes in the keyword search process, which improves the search accuracy. Moreover, we adopt a linear secret-sharing technique to construct the attribute revocation function of MKSABE-VaAR, making a lower computational cost of attribute revocation. Furthermore, the mechanism of storing data and indexes separately greatly reduces the risk of data leakage of MKSABE-VaAR.

Software Defined Networks enabled-cloud (SDN-Cloud) is experiencing rapid evolution to accommodate the explosive growth of data-driven applications. However, traditional resource allocation algorithms are encountering limitations in efficient resources management. While some existing algorithms strive to minimize power consumption, they introduce network delays, impacting overall performance. Thus, this study aims to address the prevalent challenges of performance efficiency and energy saving within distributed systems. Data mining and fuzzy logic, are increasingly utilized to develop more adaptive and intelligent resource management models. However, given the dynamic nature of SDN-cloud environments, rapid decision-making during VM allocation is essential to prevent network delays. Furthermore, the limited computational resource of SDN controller requires cautious consideration, as extensive calculations will result in network overhead or increased power consumption. Moreover, achieving subtle balance between network performance and power efficiency still an open challenge. This research introduces Dual-Phase resource allocation Algorithm (D-Ph) for heterogeneous SDN-Cloud networks with the integration of fuzzy logic. D-Ph algorithm indicates the level of utilization of both physical and virtual machines (PM and VM) in datacenters. It aims to find the appropriate host with the necessary capabilities to meet VM resource requirements, specifically processing capacity and memory. The performance of the D-Ph algorithm is evaluated by measuring the response time, serve time of network and central processing unit (CPU), Quality of Service (QoS) violation rate, and power consumption. Results have shown distinctly that D-Ph algorithm maintain high network performance while significantly reduce total power consumption in heavy-loaded large-scale network.

With the frequent occurrence of private data breaches, it is now more necessary than ever to address how to protect private data. The combination of Cipher text-Policy Attribute-Based Encryption (CP-ABE) and block chain typically enables secure storage and sharing of data. However, in high dimensional attribute domains, that is, the number of attributes is large, these schemes have issues such as low security of data protection, high computational overhead, and high cost of attribute revocation. This paper proposes a personal privacy data protection scheme for encryption and revocation of high-dimensional attribute domains to address these issues. The proposed scheme is made up of three components. Firstly, Fast High-dimensional Attribute Domain-based Message Encryption (HAD-FME) is proposed to improve data security and reduce computational cost. Secondly, an Attribute Revocation Mechanism Based on Sentry Mode (SM-ARM) is designed in combination with smart contracts. Lastly, a Block chain-based Model for Personal Privacy Data Protection (BC-PPDP) is proposed by integrating HAD-FME with SM-ARM. The security analysis results show that HAD-FME proposed in this paper is secure under the DLIN assumption, and the attribute revocation satisfies both forward and backward security. Experiments show that HADFME has higher computational efficiency than existing schemes in the high-dimensional attribute domains, SM-ARM has lower revocation cost than existing attribute revocation mechanisms, and smart contracts and block chain work well.

In the rapidly evolving realm of cloud computing security, this paper introduces an innovative solution to address persistent challenges. The proliferation of cloud technology has brought forth heightened concerns regarding data security, necessitating novel approaches to safeguarding sensitive information. The issue centres on the vulnerability of cloud-stored data, often necessitating enhanced encryption and key management strategies. Traditional methods often fall short in mitigating risks associated with compromised encryption keys and centralized key storage. To combat these challenges, our proposed solution encompasses a two-phase approach. In the first phase, dynamic Advanced Encryption Standard (AES) keys are generated, ensuring each file’s encryption with a unique and ever-changing key. This approach significantly enhances file-level security, curtailing an attacker’s ability to decrypt multiple files even if a key is compromised. The second phase introduces blockchain technology, where keys are securely stored with accompanying metadata, bolstering security and data integrity. Elliptic Curve Cryptography (ECC) public key encryption enhances security during transmission and storage, while also facilitating secure file sharing. In conclusion, this comprehensive approach enhances cloud security, providing robust encryption, decentralized key management, and protection against unauthorized access. Its scalability and adaptability make it a valuable asset in contemporary cloud security paradigms, assuring users of data security in the cloud.

Scheduling of robots is one of the imperative assignment in a multi robot system. Scheduling is prerequisite when there is a multiple task need to be assigned to multi robot in an arranged manner. There is a growing need for robots to perform complex tasks autonomously. Multi-robot environment becomes complex as there are multiple factors need to be addressed simultaneously which require fast computation and more space. Using cloud computing platform could be one of the optimal solution for this problem. This paper presents the use of cloud computing platform for implementing the proposed Periodic Min-Max Algorithm (PMW) for multi robot task scheduling. Amazon web service (AWS) platform is utilized for deploying the algorithm for multi robot task scheduling. The task performed by the robots is considered as a single service in context with cloud platform and it withdraw an advantage when the number of services increases with time. Time requirement to complete the task and the load balancing parameter are analysed using the proposed approach and is compared with other relevant work. The results presented in the paper clearly shows the performance improvement in both the parameters. There is an improvement of about 3-7% in both the parameters and are reported in the paper. The paper also emphasize on the deployment of cloud computing platform for the service robots. Time completion factor is analysed and reported in the paper to proof the advantage of using cloud platform for the service robots. The novel way of using the algorithm with cloud server seeks many advantage are also observed, analysed and presented in the paper.