Recent Advances in Security, Privacy, and Trust

Topic Information

Dear Colleagues,

The proliferation of information, communication, and computer technologies has brought us into the realm of the cyber–physical–social system (CPSS). The CPSS comprises the cyber space, physical space and social space, and their integration into such systems as the cyber–physical system (CPS), Internet of Things (IoT), social computing system, and even the system integrating all three spaces. Recently, the CPSS has brought enormous opportunities that have significantly influenced applications. However, there are increasing security, privacy, and trust concerns, such as the exposure of user privacy and business information in the CPSS. Although theories and technologies regarding security, privacy, and trust have been widely studied and applied in recent years, existing methods are still insecure, impractical or inefficient. To address these challenges, this topic solicits articles reflecting the latest research outcomes and developments in security, privacy, and trust.

The topics of interest include, but not limited to, the following:

• Privacy-enhancing technologies
• Privacy-preserving/secure/trust data analysis and processing
• Network security, privacy, and trust
• Differentially private data analysis
• Sustainable security, privacy, and trust
• Economics of security, privacy, and trust
• Blockchain and its applications
• IoT/CPS/CPSS security, privacy, and trust
• Security, privacy, and trust in edge/fog/cloud computing
• AI/Machine learning security
• Federated learning
• System security
• Hardware security
• Web security, privacy, and trust
• Big data, artificial intelligence for security, privacy, and trust
• Digital twin security, privacy, and trust
• Cryptographic techniques, cryptographic protocols

Dr. Jun Feng
Dr. Changqing Luo
Prof. Dr. Mamoun Alazab
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.6	5.3	2012	16.8 Days	CHF 2400	Submit
Journal of Cybersecurity and Privacy jcp	-	5.3	2021	32.4 Days	CHF 1000	Submit
Mathematics mathematics	2.3	4.0	2013	17.1 Days	CHF 2600	Submit
Sustainability sustainability	3.3	6.8	2009	20 Days	CHF 2400	Submit
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	6.9	2012	36.2 Days	CHF 1700	Submit
Cryptography cryptography	1.8	3.8	2017	23.9 Days	CHF 1600	Submit
Blockchains blockchains	-	-	2023	15.0 days *	CHF 1000	Submit

* Median value for all MDPI journals in the first half of 2024.

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Published Papers (15 papers)

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All Electronics JCP Mathematics Sustainability Remote Sensing IJGI Cryptography Blockchains

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23 pages, 2884 KiB  

Open AccessArticle

Privacy Preserving Human Mobility Generation Using Grid-Based Data and Graph Autoencoders

by Fabian Netzler and Markus Lienkamp

ISPRS Int. J. Geo-Inf. 2024, 13(7), 245; https://doi.org/10.3390/ijgi13070245 - 9 Jul 2024

Viewed by 449

Abstract

This paper proposes a one-to-one trajectory synthetization method with stable long-term individual mobility behavior based on a generalizable area embedding. Previous methods concentrate on producing highly detailed data on short-term and restricted areas for, e.g., autonomous driving scenarios. Another possibility consists of city-wide [...] Read more.

This paper proposes a one-to-one trajectory synthetization method with stable long-term individual mobility behavior based on a generalizable area embedding. Previous methods concentrate on producing highly detailed data on short-term and restricted areas for, e.g., autonomous driving scenarios. Another possibility consists of city-wide and beyond scales that can be used to predict general traffic flows. The now-presented approach takes the tracked mobility behavior of individuals and creates coherent synthetic mobility data. These generated data reflect the person’s long-term mobility behavior, guaranteeing location persistency and sound embedding within the point-of-interest structure of the observed area. After an analysis and clustering step of the original data, the area is distributed into a geospatial grid structure (H3 is used here). The neighborhood relationships between the grids are interpreted as a graph. A feed-forward autoencoder and a graph encoding–decoding network generate a latent space representation of the area. The original clustered data are associated with their respective H3 grids. With a greedy algorithm approach and concerning privacy strategies, new combinations of grids are generated as top-level patterns for individual mobility behavior. Based on the original data, concrete locations within the new grids are found and connected to ways. The goal is to generate a dataset that shows equivalence in aggregated characteristics and distances in comparison with the original data. The described method is applied to a sample of 120 from a study with 1000 participants whose mobility data were generated in the city of Munich in Germany. The results show the applicability of the approach in generating synthetic data, enabling further research on individual mobility behavior and patterns. The result comprises a sharable dataset on the same abstraction level as the input data, which can be beneficial for different applications, particularly for machine learning. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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17 pages, 310 KiB  

Open AccessArticle

An NTRU-like Message Recoverable Signature Algorithm

by Tingle Shen, Li Miao, Bin Hua and Shuai Li

Mathematics 2024, 12(13), 2051; https://doi.org/10.3390/math12132051 - 30 Jun 2024

Viewed by 350

Abstract

An important feature of Nyberg-Rueppel type digital signature algorithms is message recovery, this signature algorithm can recover the original information from the signature directly by the verifier in the verification phase after signing the message. However, this algorithm is currently vulnerable to quantum [...] Read more.

An important feature of Nyberg-Rueppel type digital signature algorithms is message recovery, this signature algorithm can recover the original information from the signature directly by the verifier in the verification phase after signing the message. However, this algorithm is currently vulnerable to quantum attacks and its security cannot be guaranteed. Number Theory Research Unit (NTRU) is an efficient public-key cryptosystem and is considered to be one of the best quantum-resistant encryption schemes. This paper proposes an NTRU-like message recoverable signature algorithm to meet the key agreement requirements in the post-quantum world. This algorithm, designed for the Internet of Things (IoT), constructs a secure system using the Group-Based Message Recoverable Signature Algorithm (NR-GTRU), by integrating a Group-Based NTRU-Like Public-Key Cryptosystem (GTRU) with an efficient Nyberg-Rueppel type of NTRU digital signature algorithm (NR-NTRU). This signature algorithm, resistant to quantum algorithm attacks, offers higher security at the cost of a slight efficiency reduction compared to traditional NTRU signature algorithms, and features Nyberg-Rueppel message recovery, making it well-suited for IoT applications. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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20 pages, 500 KiB  

Open AccessArticle

Distributed Group Key Management Based on Blockchain

by Jia Ni, Guowei Fang, Yekang Zhao, Jingjing Ren, Long Chen and Yongjun Ren

Electronics 2024, 13(11), 2216; https://doi.org/10.3390/electronics13112216 - 6 Jun 2024

Viewed by 409

Abstract

Against the backdrop of rapidly advancing cloud storage technology, as well as 5G and 6G communication technologies, group key management faces increasingly daunting challenges. Traditional key management encounters difficulties in key distribution, security threats, management complexity, and issues of trustworthiness. Particularly in scenarios [...] Read more.

Against the backdrop of rapidly advancing cloud storage technology, as well as 5G and 6G communication technologies, group key management faces increasingly daunting challenges. Traditional key management encounters difficulties in key distribution, security threats, management complexity, and issues of trustworthiness. Particularly in scenarios with a large number of members or frequent member turnover within groups, this may lead to security vulnerabilities such as permission confusion, exacerbating the security risks and management complexity faced by the system. To address these issues, this paper utilizes blockchain technology to achieve distributed storage and management of group keys. This solution combines key management with the distributed characteristics of blockchain, enhancing scalability, and enabling tracking of malicious members. Simultaneously, by integrating intelligent authentication mechanisms and lightweight data update mechanisms, it effectively enhances the security, trustworthiness, and scalability of the key management system. This provides important technical support for constructing a more secure and reliable network environment. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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12 pages, 3280 KiB  

Open AccessArticle

Video Detection Method Based on Temporal and Spatial Foundations for Accurate Verification of Authenticity

by Chin-Yuan Lin, Jen-Chun Lee, Shuenn-Jyi Wang, Chung-Shi Chiang and Chao-Lung Chou

Electronics 2024, 13(11), 2132; https://doi.org/10.3390/electronics13112132 - 30 May 2024

Viewed by 439

Abstract

With the rapid development of deepfake technology, it is finding applications in virtual movie production and entertainment. However, its potential for malicious use, such as generating false information, fake news, or synthetic pornography, poses significant threats to national and social security. Various research [...] Read more.

With the rapid development of deepfake technology, it is finding applications in virtual movie production and entertainment. However, its potential for malicious use, such as generating false information, fake news, or synthetic pornography, poses significant threats to national and social security. Various research disciplines are actively engaged in developing deepfake video detection technologies to mitigate the risks associated with malicious deepfake content. Therefore, the importance of deepfake video detection technology cannot be overemphasized. This study addresses the challenge posed by images in nonexistent datasets by analyzing deepfake video detection methods. Using temporal and spatial detection techniques and employing 68 facial landmarks for alignment and feature extraction, this research integrates the attention-guided data augmentation (AGDA) strategy to enhance generalization capabilities. The detection performance is evaluated on four datasets: UADFV, FaceForensics++, Celeb-DF, and DFDC, with superior results compared to alternative approaches. To evaluate the study’s ability to accurately discriminate authenticity, detection experiments are conducted on both genuine and deepfake videos synthesized using the DeepFaceLab and FakeApp frameworks. The experimental results show better performance in detecting deepfake videos than other methods compared. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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15 pages, 1833 KiB  

Open AccessArticle

Self-Knowledge Distillation via Progressive Associative Learning

by Haoran Zhao, Yanxian Bi, Shuwen Tian, Jian Wang, Peiying Zhang, Zhaopeng Deng and Kai Liu

Electronics 2024, 13(11), 2062; https://doi.org/10.3390/electronics13112062 - 25 May 2024

Viewed by 446

Abstract

As a specific form of knowledge distillation (KD), self-knowledge distillation enables a student network to progressively distill its own knowledge without relying on a pretrained, complex teacher network; however, recent studies of self-KD have discovered that additional dark knowledge captured by auxiliary architecture [...] Read more.

As a specific form of knowledge distillation (KD), self-knowledge distillation enables a student network to progressively distill its own knowledge without relying on a pretrained, complex teacher network; however, recent studies of self-KD have discovered that additional dark knowledge captured by auxiliary architecture or data augmentation could create better soft targets for enhancing the network but at the cost of significantly more computations and/or parameters. Moreover, most existing self-KD methods extract the soft label as a supervisory signal from individual input samples, which overlooks the knowledge of relationships among categories. Inspired by human associative learning, we propose a simple yet effective self-KD method named associative learning for self-distillation (ALSD), which progressively distills richer knowledge regarding the relationships between categories across independent samples. Specifically, in the process of distillation, the propagation of knowledge is weighted based on the intersample relationship between associated samples generated in different minibatches, which are progressively estimated with the current network. In this way, our ALSD framework achieves knowledge ensembling progressively across multiple samples using a single network, resulting in minimal computational and memory overhead compared to existing ensembling methods. Extensive experiments demonstrate that our ALSD method consistently boosts the classification performance of various architectures on multiple datasets. Notably, ALSD pushes forward the self-KD performance to 80.10% on CIFAR-100, which exceeds the standard backpropagation by 4.81%. Furthermore, we observe that the proposed method shows comparable performance with the state-of-the-art knowledge distillation methods without the pretrained teacher network. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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20 pages, 1370 KiB  

Open AccessArticle

Energy-Efficient Virtual Network Embedding: A Deep Reinforcement Learning Approach Based on Graph Convolutional Networks

by Peiying Zhang, Enqi Wang, Zhihu Luo, Yanxian Bi, Kai Liu and Jian Wang

Electronics 2024, 13(10), 1918; https://doi.org/10.3390/electronics13101918 - 14 May 2024

Viewed by 710

Abstract

Network virtualization (NV) technology is the cornerstone of modern network architectures, offering significant advantages in resource utilization, flexibility, security, and streamlined management. By enabling the deployment of multiple virtual network requests (VNRs) within a single base network through virtual network embedding (VNE), NV [...] Read more.

Network virtualization (NV) technology is the cornerstone of modern network architectures, offering significant advantages in resource utilization, flexibility, security, and streamlined management. By enabling the deployment of multiple virtual network requests (VNRs) within a single base network through virtual network embedding (VNE), NV technology can substantially reduce the operational costs and energy consumption. However, the existing algorithms for energy-efficient VNE have limitations, including manual tuning for heuristic routing policies, inefficient feature extraction in traditional intelligent algorithms, and a lack of consideration of periodic traffic fluctuations. To address these limitations, this paper introduces a novel approach that leverages deep reinforcement learning (DRL) to enhance the efficiency of traditional methods. We employ graph convolutional networks (GCNs) for feature extraction, capturing the nuances of network graph structures, and integrate periodic traffic fluctuations as a key constraint in our model. This allows for the predictive embedding of VNRs that is both energy-efficient and responsive to dynamic network conditions. Our research aims to develop an energy-efficient VNE algorithm that dynamically adapts to network traffic patterns, thereby optimizing resource allocation and reducing energy consumption. Extensive simulation experiments demonstrate that our proposed algorithm achieves an average reduction of 22.4% in energy consumption and 41.0% in active substrate nodes, along with a 23.4% improvement in the acceptance rate compared to other algorithms. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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24 pages, 680 KiB  

Open AccessArticle

A Privacy Protection Scheme of Certificateless Aggregate Ring Signcryption Based on SM2 Algorithm in Smart Grid

by Hongna Song, Zhentao Liu, Teng Wang, Ling Zhao, Haonan Guo and Shuanggen Liu

Mathematics 2024, 12(9), 1314; https://doi.org/10.3390/math12091314 - 25 Apr 2024

Viewed by 623

Abstract

With the rapid increase in smart grid users and the increasing cost of user data transmission, proposing an encryption method that does not increase the construction cost while increasing the user ceiling has become the focus of many scholars. At the same time, [...] Read more.

With the rapid increase in smart grid users and the increasing cost of user data transmission, proposing an encryption method that does not increase the construction cost while increasing the user ceiling has become the focus of many scholars. At the same time, the increase in users will also lead to more security problems, and it is also necessary to solve the privacy protection for users during information transmission. In order to solve the above problems, this paper proposes an aggregated ring encryption scheme based on the SM2 algorithm with special features, referred to as SM2-CLARSC, based on the certificateless ring signcryption mechanism and combining with the aggregate signcryption. SM2-CLARSC is designed to satisfy the basic needs of the smart grid, and it can be resistant to replay attacks, forward security and backward security, etc. It has better security and higher efficiency than existing solutions. Comparing SM2-CLARSC with existing typical solutions through simulation, the result proves that this solution has more comprehensive functions, higher security, and significant computational efficiency improvement. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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17 pages, 642 KiB  

Open AccessArticle

Differentiated Security Requirements: An Exploration of Microservice Placement Algorithms in Internet of Vehicles

by Xing Zhang, Jun Liang, Yuxi Lu, Peiying Zhang and Yanxian Bi

Electronics 2024, 13(8), 1597; https://doi.org/10.3390/electronics13081597 - 22 Apr 2024

Viewed by 729

Abstract

In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in [...] Read more.

In recent years, microservices, as an emerging technology in software development, have been favored by developers due to their lightweight and low-coupling features, and have been rapidly applied to the Internet of Things (IoT) and Internet of Vehicles (IoV), etc. Microservices deployed in each unit of the IoV use wireless links to transmit data, which exposes a larger attack surface, and it is precisely because of these features that the secure and efficient placement of microservices in the environment poses a serious challenge. Improving the security of all nodes in an IoV can significantly increase the service provider’s operational costs and can create security resource redundancy issues. As the application of reinforcement learning matures, it is enabling faster convergence of algorithms by designing agents, and it performs well in large-scale data environments. Inspired by this, this paper firstly models the placement network and placement behavior abstractly and sets security constraints. The environment information is fully extracted, and an asynchronous reinforcement-learning-based algorithm is designed to improve the effect of microservice placement and reduce the security redundancy based on ensuring the security requirements of microservices. The experimental results show that the algorithm proposed in this paper has good results in terms of the fit of the security index with user requirements and request acceptance rate. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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17 pages, 774 KiB  

Open AccessArticle

Trust Management Scheme of IoV Based on Dynamic Sharding Blockchain

by Hongmu Han, Sheng Chen, Zhigang Xu, Xinhua Dong and Jing Zeng

Electronics 2024, 13(6), 1016; https://doi.org/10.3390/electronics13061016 - 7 Mar 2024

Viewed by 892

Abstract

With the rapid development of communication technologies, the demand for security and automation of driving has promoted the development of the Internet of Vehicles (IoV). The IoV aims to provide users with a safer, more comfortable, and more efficient driving experience. However, the [...] Read more.

With the rapid development of communication technologies, the demand for security and automation of driving has promoted the development of the Internet of Vehicles (IoV). The IoV aims to provide users with a safer, more comfortable, and more efficient driving experience. However, the current IoV also faces a series of potential security risks and privacy breaches, which has further propelled research on trust management for vehicular networks. The introduction of the blockchain has resolved the issue of data security in IoV trust management. However, the blockchain is limited by its own performance and scalability, making it unsuitable for large-scale networks. In order to enhance the transaction-processing efficiency of blockchain-based trust management solutions and address their scalability limitations, this paper presents a graph partition-based blockchain-sharding protocol. Simulation results on real-world datasets demonstrate that the proposed scheme exhibits better scalability compared to existing blockchain-based approaches and can accommodate larger-scale device access. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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13 pages, 4658 KiB  

Open AccessArticle

A Sampling-Based Method for Detecting Data Poisoning Attacks in Recommendation Systems

by Mohan Li, Yuxin Lian, Jinpeng Zhu, Jingyi Lin, Jiawen Wan and Yanbin Sun

Mathematics 2024, 12(2), 247; https://doi.org/10.3390/math12020247 - 12 Jan 2024

Cited by 2 | Viewed by 1209

Abstract

The recommendation algorithm based on collaborative filtering is vulnerable to data poisoning attacks, wherein attackers can manipulate system output by injecting a large volume of fake rating data. To address this issue, it is essential to investigate methods for detecting systematically injected poisoning [...] Read more.

The recommendation algorithm based on collaborative filtering is vulnerable to data poisoning attacks, wherein attackers can manipulate system output by injecting a large volume of fake rating data. To address this issue, it is essential to investigate methods for detecting systematically injected poisoning data within the rating matrix. Since attackers often inject a significant quantity of poisoning data in a short period to achieve their desired impact, these data may exhibit spatial proximity. In other words, poisoning data may be concentrated in adjacent rows of the rating matrix. This paper capitalizes on the proximity characteristics of poisoning data in the rating matrix and introduces a sampling-based method for detecting data poisoning attacks. First, we designed a rating matrix sampling method specifically for detecting poisoning data. By sampling differences obtained from the original rating matrix, it is possible to infer the presence of poisoning attacks and effectively discard poisoning data. Second, we developed a method for pinpointing malicious data based on the distance of rating vectors. Through distance calculations, we can accurately identify the positions of malicious data. After that, we validated the method on three real-world datasets. The results demonstrate the effectiveness of our method in identifying malicious data within the rating matrix. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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18 pages, 2524 KiB  

Open AccessArticle

A Routing Strategy Based Genetic Algorithm Assisted by Ground Access Optimization for LEO Satellite Constellations

by Peiying Zhang, Chong Lv, Guanjun Xu, Haoyu Wang, Lizhuang Tan and Kostromitin Konstantin Igorevich

Electronics 2023, 12(23), 4762; https://doi.org/10.3390/electronics12234762 - 24 Nov 2023

Viewed by 1041

Abstract

Large-scale low Earth orbit satellite networks (LSNs) have been attracting increasing attention in recent years. These systems offer advantages such as low latency, high bandwidth communication, and all terrain coverage. However, the main challenges faced by LSNs is the calculation and maintenance of [...] Read more.

Large-scale low Earth orbit satellite networks (LSNs) have been attracting increasing attention in recent years. These systems offer advantages such as low latency, high bandwidth communication, and all terrain coverage. However, the main challenges faced by LSNs is the calculation and maintenance of routing strategies. This is primarily due to the large scale and dynamic network topology of LSN constellations. As the number of satellites in constellations continues to rise, the feasibility of the centralized routing strategy, which calculates all shortest routes between every satellite, becomes increasingly limited by space and time constraints. This approach is also not suitable for the Walker Delta formation, which is becoming more popular for giant constellations. In order to find an effective routing strategy, this paper defines the satellite routing problem as a mixed linear integer programming problem (MILP), proposes a routing strategy based on a genetic algorithm (GA), and comprehensively considers the efficiency of source or destination ground stations to access satellite constellations. The routing strategy integrates ground station ingress and exit policies and inter-satellite packet forwarding policies and reduces the cost of routing decisions. The experimental results show that, compared with the traditional satellite routing algorithm, the proposed routing strategy has better link capacity utilization, a lower round trip communication time, and an improved traffic reception rate. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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18 pages, 2119 KiB  

Open AccessArticle

A Certificateless Online/Offline Aggregate Signcryption Scheme against Collusion Attacks Based on Fog Computing

by Wanju Zhang, Shuanggen Liu, Yaowei Liu, Junjie Cao, Bingqi Fu and Yun Du

Electronics 2023, 12(23), 4747; https://doi.org/10.3390/electronics12234747 - 23 Nov 2023

Viewed by 831

Abstract

The certificateless online/offline aggregate signcryption scheme combines the characteristics of the certificateless aggregate signcryption scheme and the online/offline encryption scheme, which can increase efficiency while simultaneously reducing consumption. Some schemes can meet the requirements of confidentiality and real-time transmission of the data in [...] Read more.

The certificateless online/offline aggregate signcryption scheme combines the characteristics of the certificateless aggregate signcryption scheme and the online/offline encryption scheme, which can increase efficiency while simultaneously reducing consumption. Some schemes can meet the requirements of confidentiality and real-time transmission of the data in ad hoc networks (VANETS). However, they are unable to withstand collusion attempts. A brand-new certificateless aggregate signcryption approach is suggested to overcome this problem. First, combining fog computing with online/offline encryption (OOE) technology can increase efficiency while simultaneously reducing consumption. Second, we may achieve effective information authentication and vehicle identification using aggregation and vehicle pseudonym systems. Third, the anti-collusion component is suggested as a viable defense against collusion assaults since certain methods are unable to withstand such attacks. Additionally, it is demonstrated that the technique has unforgeability and secrecy, and can fend off collusion attacks using the random oracle model. The findings demonstrate that our system can not only ensure the confidentiality and the real-time transmission of data but also resist collusion attacks without raising computational costs. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)
(This article belongs to the Section Electrical and Autonomous Vehicles)

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22 pages, 976 KiB  

Open AccessArticle

Flow-Based Joint Programming of Time Sensitive Task and Network

by Yingying Chi, Huayu Zhang, Yong Liu, Ning Chen, Zhe Zheng, Hailong Zhu, Peiying Zhang and Haotian Zhan

Electronics 2023, 12(19), 4103; https://doi.org/10.3390/electronics12194103 - 30 Sep 2023

Cited by 1 | Viewed by 1044

Abstract

Owning to the application of artificial intelligence and big data analysis in industry, automobiles, aerospace, and other fields, the high-bandwidth candidate, time-sensitive networking (TSN), is introduced into the data communication network. Apart from keeping the safety-critical and real-time requirements, it faces challenges to [...] Read more.

Owning to the application of artificial intelligence and big data analysis in industry, automobiles, aerospace, and other fields, the high-bandwidth candidate, time-sensitive networking (TSN), is introduced into the data communication network. Apart from keeping the safety-critical and real-time requirements, it faces challenges to satisfy large traffic transmission, such as sampled video for computer vision. In this paper, we consider task scheduling and time-sensitive network together and formalize them into a first-order-constraints satisfy module theory (SMT) problem. Based on the result of the solver, we build flow-level scheduling based on IEEE 802.1 Qbv. By splitting the flow properly, it can reduce the constraint inequality as the traffic grows more than the traditional frame-based programming model and achieve near 100% utilization. It can be a general model for the deterministic task and network scheduling design. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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22 pages, 1121 KiB  

Open AccessArticle

Network Resource Allocation Algorithm Using Reinforcement Learning Policy-Based Network in a Smart Grid Scenario

by Zhe Zheng, Yu Han, Yingying Chi, Fusheng Yuan, Wenpeng Cui, Hailong Zhu, Yi Zhang and Peiying Zhang

Electronics 2023, 12(15), 3330; https://doi.org/10.3390/electronics12153330 - 3 Aug 2023

Cited by 4 | Viewed by 1216

Abstract

The exponential growth in user numbers has resulted in an overwhelming surge in data that the smart grid must process. To tackle this challenge, edge computing emerges as a vital solution. However, the current heuristic resource scheduling approaches often suffer from resource fragmentation [...] Read more.

The exponential growth in user numbers has resulted in an overwhelming surge in data that the smart grid must process. To tackle this challenge, edge computing emerges as a vital solution. However, the current heuristic resource scheduling approaches often suffer from resource fragmentation and consequently get stuck in local optimum solutions. This paper introduces a novel network resource allocation method for multi-domain virtual networks with the support of edge computing. The approach entails modeling the edge network as a multi-domain virtual network model and formulating resource constraints specific to the edge computing network. Secondly, a policy network is constructed for reinforcement learning (RL) and an optimal resource allocation strategy is obtained under the premise of ensuring resource requirements. In the experimental section, our algorithm is compared with three other algorithms. The experimental results show that the algorithm has an average increase of 5.30%, 8.85%, 15.47% and 22.67% in long-term average revenue–cost ratio, virtual network request acceptance ratio, long-term average revenue and CPU resource utilization, respectively. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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14 pages, 772 KiB  

Open AccessArticle

Node Selection Algorithm for Federated Learning Based on Deep Reinforcement Learning for Edge Computing in IoT

by Shuai Yan, Peiying Zhang, Siyu Huang, Jian Wang, Hao Sun, Yi Zhang and Amr Tolba

Electronics 2023, 12(11), 2478; https://doi.org/10.3390/electronics12112478 - 31 May 2023

Cited by 2 | Viewed by 1868

Abstract

The Internet of Things (IoT) and edge computing technologies have been rapidly developing in recent years, leading to the emergence of new challenges in privacy and security. Personal privacy and data leakage have become major concerns in IoT edge computing environments. Federated learning [...] Read more.

The Internet of Things (IoT) and edge computing technologies have been rapidly developing in recent years, leading to the emergence of new challenges in privacy and security. Personal privacy and data leakage have become major concerns in IoT edge computing environments. Federated learning has been proposed as a solution to address these privacy issues, but the heterogeneity of devices in IoT edge computing environments poses a significant challenge to the implementation of federated learning. To overcome this challenge, this paper proposes a novel node selection strategy based on deep reinforcement learning to optimize federated learning in heterogeneous device IoT environments. Additionally, a metric model for IoT devices is proposed to evaluate the performance of different devices. The experimental results demonstrate that the proposed method can improve training accuracy by 30% in a heterogeneous device IoT environment. Full article

(This article belongs to the Topic Recent Advances in Security, Privacy, and Trust)

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