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
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Electronics electronics	2.6	5.3	2012	16.4 Days	CHF 2400	Submit
Journal of Cybersecurity and Privacy jcp	-	5.3	2021	26.9 Days	CHF 1000	Submit
Mathematics mathematics	2.3	4.0	2013	18.3 Days	CHF 2600	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
ISPRS International Journal of Geo-Information ijgi	2.8	6.9	2012	35.8 Days	CHF 1900	Submit
Cryptography cryptography	1.8	3.8	2017	26.9 Days	CHF 1600	Submit
Blockchains blockchains	-	-	2023	15.0 days *	CHF 1000	Submit

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

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Published Papers (28 papers)

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All Electronics JCP Mathematics Sustainability Remote Sensing IJGI Cryptography Blockchains

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22 pages, 1198 KiB  

Open AccessArticle

Malicious-Secure Threshold Multi-Party Private Set Intersection for Anonymous Electronic Voting

by Xiansong Qian, Lifei Wei, Jinjiao Zhang and Lei Zhang

Cryptography 2025, 9(2), 23; https://doi.org/10.3390/cryptography9020023 - 17 Apr 2025

Viewed by 179

Abstract

Threshold Multi-Party Private Set Intersection (TMP-PSI) is a cryptographic protocol that enables an element from the receiver’s set to be included in the intersection result if it appears in the sets of at least $t-1$ other participants, where t represents the [...] Read more.

Threshold Multi-Party Private Set Intersection (TMP-PSI) is a cryptographic protocol that enables an element from the receiver’s set to be included in the intersection result if it appears in the sets of at least $t-1$ other participants, where t represents the threshold. This protocol is crucial for a variety of applications, such as anonymous electronic voting, online ride-sharing, and close-contact tracing programs. However, most existing TMP-PSI schemes are designed based on threshold homomorphic encryption, which faces significant challenges, including low computational efficiency and a high number of communication rounds. To overcome these limitations, this study introduces the Threshold Oblivious Pseudo-Random Function (tOPRF) to fulfill the requirements of threshold encryption and decryption. Additionally, we extend the concept of the Oblivious Programmable Pseudo-Random Function (OPPRF) to develop a novel cryptographic primitive termed the Partially OPPRF (P-OPPRF). This new primitive retains the critical properties of obliviousness and randomness, along with the security assurances inherited from the OPPRF, while also offering strong resistance against malicious adversaries. Leveraging this primitive, we propose the first malicious-secure TMP-PSI protocol, named QMP-PSI, specifically designed for applications like anonymous electronic voting systems. The protocol effectively counters collusion attacks among multiple parties, ensuring robust security in multi-party environments. To further enhance voting efficiency, this work presents a cloud-assisted QMP-PSI to outsource the computationally intensive phases. This ensures that the computational overhead for participants is solely dependent on the set size and statistical security parameters, thereby maintaining security while significantly reducing the computational burden on voting participants. Finally, this work validates the protocol’s performance through extensive experiments under various set sizes, participant numbers, and threshold values. The results demonstrate that the protocol surpasses existing schemes, achieving state-of-the-art (SOTA) performance in communication overhead. Notably, in small-scale voting scenarios, it exhibits exceptional performance, particularly when the threshold is small or close to the number of participants. Full article

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

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15 pages, 1348 KiB  

Open AccessArticle

HRRnet: A Parameter Estimation Method for Linear Frequency Modulation Signals Based on High-Resolution Spectral Line Representation

by Shunchao Fei, Mengqing Yan, Fan Zhou, Yang Wang, Peiying Zhang, Jian Wang and Wei Wang

Electronics 2025, 14(6), 1121; https://doi.org/10.3390/electronics14061121 - 12 Mar 2025

Viewed by 478

Abstract

Under the condition of low SNR, enhancing the precision of parameter estimation for linear frequency modulation (LFM) signals and diminishing the complexity of the relevant methods represent crucial challenges that are presently being confronted. To address this problem, a parameter estimation method for [...] Read more.

Under the condition of low SNR, enhancing the precision of parameter estimation for linear frequency modulation (LFM) signals and diminishing the complexity of the relevant methods represent crucial challenges that are presently being confronted. To address this problem, a parameter estimation method for LFM signals based on the High-Resolution Representation network (HRRnet) is proposed. The fundamental concept underlying this method lies in the employment of a strategy that combines the expansion of the receptive field with the fusion of multi-scale features. This enables the efficient extraction of both global and local information, which in turn augments the expressive power of the inherent signal characteristics and consequently mitigates the impact of noise interference. Based on this strategy, a high-resolution representation of the time–frequency spectrum of the signals is performed to improve the distinguishability of the time–frequency spectrum, and it further improve the accuracy of parameter estimation for LFM signals. In addition, the network utilizes dilated convolution to expand the receptive field while reducing the dependence on network depth, so as to control the network complexity and further optimize the computational efficiency. Experimental results show that when the SNR is greater than −12 dB and the tolerable error is equal to 0.1, the average accuracy of the HRRnet method for estimating the initial frequency and frequency modulation coefficient of LFM signals can reach above 95.53% and 91.19%, respectively, and its number of parameters and computational complexity are reduced to more than 20.47% and 20.37% of those of the existing methods. Full article

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

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25 pages, 20387 KiB  

Open AccessArticle

Connecting Visual Data to Privacy: Predicting and Measuring Privacy Risks in Images

by Hongpu Jiang, Jinxin Zuo and Yueming Lu

Electronics 2025, 14(4), 811; https://doi.org/10.3390/electronics14040811 - 19 Feb 2025

Viewed by 459

Abstract

More and more users openly share their information on online websites, with the resulting privacy issues being under scrutiny. Content such as a user’s personal data and location information is often asked for before posting to enforce the user’s privacy preferences; however, little [...] Read more.

More and more users openly share their information on online websites, with the resulting privacy issues being under scrutiny. Content such as a user’s personal data and location information is often asked for before posting to enforce the user’s privacy preferences; however, little attention has been paid to the lack of content (e.g., images) posted by the user. Even if privacy preferences are requested before images are published, publishers often remain unaware of the extent of privacy leakage associated with their data. To this end, we provide an image privacy metric scheme that incorporates users’ privacy preferences, with the core idea of assisting users in making data publishing decisions. First, we propose privacy-specific spatial attention mechanisms that can effectively improve the prediction accuracy. Next, we integrate set pair analysis (SPA) theory and use the network output as the privacy value. Finally, we combine a user study to understand the privacy preferences of different users with respect to these attributes and combine it with principal component analysis to correct and enforce user privacy preferences. Our model is trained with the ability to predict privacy risk end-to-end, thus being able to guide the user in sharing data in open platforms. We use the image privacy dataset, VISPR, to predict privacy information better than other methods. Full article

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

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9 pages, 6245 KiB  

Open AccessArticle

Multi-Instance Zero-Watermarking Algorithm for Vector Geographic Data

by Qifei Zhou, Lin Yan, Zihao Wang, Na Ren and Changqing Zhu

ISPRS Int. J. Geo-Inf. 2025, 14(2), 54; https://doi.org/10.3390/ijgi14020054 - 30 Jan 2025

Viewed by 547

Abstract

To address the variability and complexity of attack types, this paper proposes a multi-instance zero-watermarking algorithm that goes beyond the conventional one-to-one watermarking approach. Inspired by the class-instance paradigm in object-oriented programming, this algorithm constructs multiple zero watermarks from a single vector geographic [...] Read more.

To address the variability and complexity of attack types, this paper proposes a multi-instance zero-watermarking algorithm that goes beyond the conventional one-to-one watermarking approach. Inspired by the class-instance paradigm in object-oriented programming, this algorithm constructs multiple zero watermarks from a single vector geographic dataset to enhance resilience against diverse attacks. Normalization is applied to eliminate dimensional and deformation inconsistencies, ensuring robustness against non-uniform scaling attacks. Feature triangle construction and angle selection are further utilized to provide resistance to interpolation and compression attacks. Moreover, angular features confer robustness against translation, uniform scaling, and rotation attacks. Experimental results demonstrate the superior robustness of the proposed algorithm, with normalized correlation values consistently maintaining 1.00 across various attack scenarios. Compared with existing methods, the algorithm exhibits superior comprehensive robustness, effectively safeguarding the copyright of vector geographic data. Full article

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

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30 pages, 5698 KiB  

Open AccessArticle

A Blockchain Copyright Protection Model Based on Vector Map Unique Identification

by Heyan Wang, Nannan Tang, Changqing Zhu, Na Ren and Changhong Wang

ISPRS Int. J. Geo-Inf. 2025, 14(2), 53; https://doi.org/10.3390/ijgi14020053 - 30 Jan 2025

Viewed by 771

Abstract

Combining blockchain technology with digital watermarking presents an efficient solution for safeguarding vector map files. However, the large data volume and stringent confidentiality requirements of vector maps pose significant challenges for direct registration on blockchain platforms. To overcome these limitations, this paper proposes [...] Read more.

Combining blockchain technology with digital watermarking presents an efficient solution for safeguarding vector map files. However, the large data volume and stringent confidentiality requirements of vector maps pose significant challenges for direct registration on blockchain platforms. To overcome these limitations, this paper proposes a blockchain-based copyright protection model utilizing unique identifiers (BCPM-UI). The model employs a distance ratio-based quantization watermarking algorithm to embed watermark information into vector maps and then generates unique identifiers based on their topological and geometric parameters. These identifiers, rather than the vector maps themselves, are securely registered on the blockchain. To ensure reliable copyright verification, a bit error rate (BER)-based matching algorithm is introduced, enabling accurate comparison between the unique identifiers of suspected infringing data and those stored on the blockchain. Experimental results validate the model’s effectiveness, demonstrating the high uniqueness and robustness of the identifiers generated. Additionally, the proposed approach reduces blockchain storage requirements for map data by a factor of 200, thereby meeting confidentiality standards while maintaining practical applicability in terms of copyright protection for vector maps. Full article

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

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18 pages, 525 KiB  

Open AccessArticle

Security Proof of Single-Source Shortest Distance Protocols Built on Secure Multiparty Computation Protocols

by Mohammad Anagreh and Peeter Laud

Cryptography 2025, 9(1), 1; https://doi.org/10.3390/cryptography9010001 - 26 Dec 2024

Viewed by 916

Abstract

Secure secret-sharing Single-Source Shortest Distance (SSSD) protocols, based on secure multiparty computation (SMC), offer a promising solution for securely distributing and managing sensitive information among multiple parties. However, formal security proofs for these protocols have largely been unexplored. This paper addresses this gap [...] Read more.

Secure secret-sharing Single-Source Shortest Distance (SSSD) protocols, based on secure multiparty computation (SMC), offer a promising solution for securely distributing and managing sensitive information among multiple parties. However, formal security proofs for these protocols have largely been unexplored. This paper addresses this gap by providing the first security proof for the SSSD protocols using the privacy-preserving Bellman–Ford protocols. These new protocols offer significant enhancements in efficiency, particularly in handling large-scale graphs due to parallel computation. In our previous work, published in MDPI Cryptography, we introduced these protocols and presented extensive experiments on the Sharemind system that demonstrated their efficiency. However, that work did not include security proofs. Building on this foundation, the current paper rigorously proves the security of these protocols, offering valuable insights into their robustness and reliability. Furthermore, we discuss the adversarial model, security definitions, cryptographic assumptions, and sophisticated reduction techniques employed in the proof. This paper not only validates the security of the proposed protocols but also provides a detailed comparison of their performance with existing methods, highlighting their strengths and potential for future research in the field. Full article

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

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15 pages, 2958 KiB  

Open AccessArticle

CFGAN: A Conditional Filter Generative Adversarial Network for Signal Modulation Mode Recognition

by Fan Zhou, Jiayi Wang, Lan Zhang, Yang Wang, Xi Chen and Peiying Zhang

Electronics 2025, 14(1), 12; https://doi.org/10.3390/electronics14010012 - 24 Dec 2024

Viewed by 543

Abstract

Currently, in the application scenario of generative adversarial networks, determining how to improve the quality of the generated signals and ensure the modulation recognition accuracy of convolutional neural networks are important problems. In this paper, a generative sample quality screening method for the [...] Read more.

Currently, in the application scenario of generative adversarial networks, determining how to improve the quality of the generated signals and ensure the modulation recognition accuracy of convolutional neural networks are important problems. In this paper, a generative sample quality screening method for the problem of low-quality samples generated by generative adversarial networks under few-shot conditions has been proposed, which innovatively establishes a sample expansion mode without fixing the network parameters, realizes the learning of the real data distribution by constantly updating the network weights, and enhances the quality of the expanded samples by adopting the quality screening method with two quality screenings. A generative adversarial network has been designed for this method, which reduces the time investment required for generating samples by extracting different features of few-shots of signals. The experimental results show the few-shot conditions, under the signal-to-noise ratio of −8∼12 dB and three expansion ratios of 1:1, 1:2 and 1:3. Compared with the general method expansion, the average modulation mode recognition accuracy of the QCO-CFGAN method expanded with the quality screening method is improved by 2.65%, 2.46% and 2.73%, respectively, which proves the effectiveness under this condition. Full article

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

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15 pages, 1058 KiB  

Open AccessArticle

CoCENet: Collaborative Channel Estimation Network for Rapidly Time-Varying UAV Systems

by Fan Zhou, Jilong Zhang, Ming Yang, Yixuan Xing, Kai Liu and Peiying Zhang

Electronics 2024, 13(23), 4702; https://doi.org/10.3390/electronics13234702 - 28 Nov 2024

Cited by 1 | Viewed by 1025

Abstract

Wireless communication capability is vital for remote transmission between unmanned aerial vehicle (UAV) coordination systems, but the communication link between highly dynamic UAV systems is unstable and severely disturbed due to its rapidly time-varying channel. Current channel estimation methods suffer from insufficient inter-symbol [...] Read more.

Wireless communication capability is vital for remote transmission between unmanned aerial vehicle (UAV) coordination systems, but the communication link between highly dynamic UAV systems is unstable and severely disturbed due to its rapidly time-varying channel. Current channel estimation methods suffer from insufficient inter-symbol interference (ISI) and inter-carrier interference (ICI) suppression and inadequate noise filtering. Therefore, a collaborative channel estimation network (CoCENet) is proposed in this paper, and it can restrain the channel interference by capturing the amplitude–phase and time–frequency correlation at the same time. Moreover, CoCENet applies a multi-scale fusion strategy to optimize the purity of the estimated outcome. Our experiment results demonstrate that CoCENet has preferable performance in terms of the suppression of channel interference and noise in rapidly time-varying UAV systems in a complex environment without stationarity assumption. At a signal-to-noise ratio (SNR) of −10 dB, the mean square error (MSE) of CoCENet is improved by 1.7–2.3 dB compared to existing methods, and at a SNR of 20 dB, the MSE is improved by 1.1–2.2 dB. Full article

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

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10 pages, 232 KiB  

Open AccessArticle

Combined and General Methodologies of Key Space Partition for the Cryptanalysis of Block Ciphers

by Mijail Borges-Quintana, Miguel A. Borges-Trenard, Osmani Tito-Corrioso, Omar Rojas and Guillermo Sosa-Gómez

Cryptography 2024, 8(4), 45; https://doi.org/10.3390/cryptography8040045 - 11 Oct 2024

Viewed by 1506

Abstract

This paper proposes two new methods of key space partitioning for the cryptanalysis of block ciphers. The first one is called combined methodology of key space partition (CoMeKSPar), which allows us to simultaneously set some of the first and last consecutive bits of [...] Read more.

This paper proposes two new methods of key space partitioning for the cryptanalysis of block ciphers. The first one is called combined methodology of key space partition (CoMeKSPar), which allows us to simultaneously set some of the first and last consecutive bits of the key. In this way, the search is performed using the remaining middle bits. CoMeKSPar is a combination of two methods already proposed in the scientific literature, the Borges, Borges, Monier (BBM) and the Tito, Borges, Borges (TBB). The second method is called the general algorithm of key space reduction (GAKSRed), which makes it possible to perform a genetic algorithm search in the space formed by the unknown bits of the key, regardless of their distribution in the binary block. Furthermore, a method of attacking block ciphers is presented for the case where some key bits are known; the basic idea is to deduce some of the remaining bits of the block. An advantage of these methods is that they allow parallel computing, which allows simultaneous searches in different sub-blocks of key bits, thereby increasing the probability of success. The experiments are performed with the KLEIN (Small) lightweight block cipher using the genetic algorithm. Full article

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

24 pages, 6069 KiB  

Open AccessArticle

Commutative Encryption and Reversible Watermarking Algorithm for Vector Maps Based on Virtual Coordinates

by Qianyi Dai, Baiyan Wu, Fanshuo Liu, Zixuan Bu and Haodong Zhang

ISPRS Int. J. Geo-Inf. 2024, 13(9), 338; https://doi.org/10.3390/ijgi13090338 - 22 Sep 2024

Cited by 2 | Viewed by 1024

Abstract

The combination of encryption and digital watermarking technologies is an increasingly popular approach to achieve full lifecycle data protection. Recently, reversible data hiding in the encrypted domain (RDHED) has greatly aroused the interest of many scholars. However, the fixed order of first encryption [...] Read more.

The combination of encryption and digital watermarking technologies is an increasingly popular approach to achieve full lifecycle data protection. Recently, reversible data hiding in the encrypted domain (RDHED) has greatly aroused the interest of many scholars. However, the fixed order of first encryption and then watermarking makes these algorithms unsuitable for many applications. Commutative encryption and watermarking (CEW) technology realizes the flexible combination of encryption and watermarking, and suits more applications. However, most existing CEW schemes for vector maps are not reversible and are unsuitable for high-precision maps. To solve this problem, here, we propose a commutative encryption and reversible watermarking (CERW) algorithm for vector maps based on virtual coordinates that are uniformly distributed on the number axis. The CERW algorithm consists of a virtual interval step-based encryption scheme and a coordinate difference-based reversible watermarking scheme. In the encryption scheme, the map coordinates are moved randomly by multiples of virtual interval steps defined as the distance between two adjacent virtual coordinates. In the reversible watermarking scheme, the difference expansion (DE) technique is used to embed the watermark bit into the coordinate difference, computed based on the relative position of a map coordinate in a virtual interval. As the relative position of a map coordinate in a virtual interval remains unchanged during the coordinate scrambling encryption process, the watermarking and encryption operations do not interfere with each other, and commutativity between encryption and watermarking is achieved. The results show that the proposed method has high security, high capacity, and good invisibility. In addition, the algorithm applies not only to polyline and polygon vector data, but also to sparsely distributed point data, which traditional DE watermarking algorithms often fail to watermark. Full article

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

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20 pages, 873 KiB  

Open AccessArticle

An Efficient and Expressive Fully Policy-Hidden Ciphertext-Policy Attribute-Based Encryption Scheme for Satellite Service Systems

by Jiaoli Shi, Chao Hu, Shunli Zhang, Qing Zhou, Zhuolin Mei, Shimao Yao and Anyuan Deng

ISPRS Int. J. Geo-Inf. 2024, 13(9), 321; https://doi.org/10.3390/ijgi13090321 - 5 Sep 2024

Viewed by 919

Abstract

Satellite service systems transfer data from satellite providers to the big data industry, which includes data traders and data analytics companies. This system needs to provide access to numerous users whose specific identities are unknown. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) allows unidentified users with [...] Read more.

Satellite service systems transfer data from satellite providers to the big data industry, which includes data traders and data analytics companies. This system needs to provide access to numerous users whose specific identities are unknown. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) allows unidentified users with the proper attributes to decrypt data, providing fine-grained access control of data. However, traditional CP-ABE does not protect access policies. Access policies are uploaded to the cloud, stored, and downloaded in plain text, making them vulnerable to privacy breaches. When the access policy is completely hidden, users need to use their own attributes to try matching one by one, which is an inefficient process. In order to efficiently hide the access policy fully, this paper introduces a new efficient and expressive Fully Policy-Hidden Ciphertext-Policy Attribute-Based Encryption scheme (CP-ABE-FPH), which integrates the 2-way handshake O-PSI method with the ROBDD method. The integration offers advantages: (1) High efficiency and high expressiveness. The access policy using ROBDD is highly expressive but computationally intensive due to its recursive nature. This shortcoming is overcome in CP-ABE-FPH using the proposed O-PSI method, and the access policy is matched quickly and secretly. (2) High flexibility. The decryption process does not require the owner or the Key Generation Center (KGC) to be online, and system attributes can be added at any time. Security analysis shows that the access policy is fully hidden. Efficiency analysis and simulation results show that the proposed scheme is highly efficient in decryption compared with existing schemes. Full article

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

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19 pages, 1891 KiB  

Open AccessArticle

Efficient and Verifiable Range Query Scheme for Encrypted Geographical Information in Untrusted Cloud Environments

by Zhuolin Mei, Jing Zeng, Caicai Zhang, Shimao Yao, Shunli Zhang, Haibin Wang, Hongbo Li and Jiaoli Shi

ISPRS Int. J. Geo-Inf. 2024, 13(8), 281; https://doi.org/10.3390/ijgi13080281 - 11 Aug 2024

Viewed by 1075

Abstract

With the rapid development of geo-positioning technologies, location-based services have become increasingly widespread. In the field of location-based services, range queries on geographical data have emerged as an important research topic, attracting significant attention from academia and industry. In many applications, data owners [...] Read more.

With the rapid development of geo-positioning technologies, location-based services have become increasingly widespread. In the field of location-based services, range queries on geographical data have emerged as an important research topic, attracting significant attention from academia and industry. In many applications, data owners choose to outsource their geographical data and range query tasks to cloud servers to alleviate the burden of local data storage and computation. However, this outsourcing presents many security challenges. These challenges include adversaries analyzing outsourced geographical data and query requests to obtain privacy information, untrusted cloud servers selectively querying a portion of the outsourced data to conserve computational resources, returning incorrect search results to data users, and even illegally modifying the outsourced geographical data, etc. To address these security concerns and provide reliable services to data owners and data users, this paper proposes an efficient and verifiable range query scheme (EVRQ) for encrypted geographical information in untrusted cloud environments. EVRQ is constructed based on a map region tree, 0–1 encoding, hash function, Bloom filter, and cryptographic multiset accumulator. Extensive experimental evaluations demonstrate the efficiency of EVRQ, and a comprehensive analysis confirms the security of EVRQ. Full article

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

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20 pages, 553 KiB  

Open AccessArticle

Spatio-Temporal Big Data Collaborative Storage Mechanism Based on Incremental Aggregation Subvector Commitment in On-Chain and Off-Chain Systems

by Mingjia Han, Xinyi Yang, Huachang Su, Yekang Zhao, Ding Huang and Yongjun Ren

ISPRS Int. J. Geo-Inf. 2024, 13(8), 280; https://doi.org/10.3390/ijgi13080280 - 10 Aug 2024

Cited by 1 | Viewed by 1360

Abstract

As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an [...] Read more.

As mobile internet and Internet of Things technologies rapidly advance, the amount of spatio-temporal big data have surged, and efficient and secure management solutions are urgently needed. Although cloud storage provides convenience, it also brings significant data security challenges. Blockchain technology is an ideal choice for processing large-scale spatio-temporal big data due to its unique security features, but its storage scalability is limited because the data need to be replicated throughout the network. To solve this problem, a common approach is to combine blockchain with off-chain storage to form a hybrid storage blockchain. However, these solutions cannot guarantee the authenticity, integrity, and consistency of on-chain and off-chain data storage, and preprocessing is required in the setup phase to generate public parameters proportional to the data length, which increases the computational burden and reduces transmission efficiency. Therefore, this paper proposes a collaborative storage mechanism for spatio-temporal big data based on incremental aggregation sub-vector commitments, which uses vector commitment binding technology to ensure the secure storage of on-chain and off-chain data. By generating public parameters of fixed length, the computational complexity is reduced and the communication efficiency is improved while improving the security of the system. In addition, we design an aggregation proof protocol that integrates aggregation algorithms and smart contracts to improve the efficiency of data query and verification and ensure the consistency and integrity of spatio-temporal big data storage. Finally, simulation experiments verify the correctness and security of the proposed protocol, providing a solid foundation for the blockchain-based spatio-temporal big data storage system. Full article

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

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22 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 1643

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 1234

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

Cited by 2 | Viewed by 1776

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

Cited by 4 | Viewed by 2003

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

Cited by 4 | Viewed by 1475

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

Cited by 1 | Viewed by 1415

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 1273

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

Cited by 2 | Viewed by 1531

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

Cited by 3 | Viewed by 1779

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 3 | Viewed by 2736

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

Cited by 1 | Viewed by 1642

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

Cited by 2 | Viewed by 1301

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 2 | Viewed by 1602

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 2106

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 7 | Viewed by 2812

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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