Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.6
Journals
Electronics
Special Issues
Novel Methods Applied to Security and Privacy Problems, Volume II
share announcement

Novel Methods Applied to Security and Privacy Problems, Volume II

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 September 2025 | Viewed by 6462

Special Issue Editors

Dr. Yongjun Ren

E-Mail Website
Guest Editor
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: network security and applied cryptography
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hu Xiong

E-Mail Website
Guest Editor
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
Interests: information security and cryptography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue presents the latest research findings on novel theories and approaches to security and privacy. Over the years, researchers have tried to break through traditional security and privacy methods and have made a lot of progress, for instance, with quantum computers, which has severely challenged traditional conservation methods. Moreover, with continuous improvements in security- and privacy-related laws, the standard of protection is also constantly improving. In order to meet the requirements of the new era and cope with the ever-changing means of attack, it is necessary to develop new, non-traditional methods and innovate traditional methods, such as lattice-based, zero-knowledge proof, blockchain, and secure deep learning and machine learning. We welcome the latest research findings that suggest theories and practical solutions for security and privacy.

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

  1. Lattice-based methods;
  2. Zero-knowledge proof and secure multi-party computation;
  3. Machine learning with novel secure privacy protection;
  4. Blockchain with novel secure privacy protection;
  5. Internet of Things with novel secure protection;
  6. Other innovative security and privacy protection methods.

Dr. Yongjun Ren
Prof. Dr. Hu Xiong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • lattice-based method
  • zero-knowledge proof
  • machine learning with novel secure privacy protection method
  • blockchain with novel secure privacy protection method
  • Internet of Things (IoT) with novel secure protection method 
  • other innovative security and privacy protection method

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 4109 KiB  
Article
AI-Based Malicious Encrypted Traffic Detection in 5G Data Collection and Secure Sharing
by Gang Han, Haohe Zhang, Zhongliang Zhang, Yan Ma and Tiantian Yang
Electronics 2025, 14(1), 51; https://doi.org/10.3390/electronics14010051 - 26 Dec 2024
Viewed by 963
Abstract
With the development and widespread application of network information, new technologies led by 5G are emerging, resulting in an increasingly complex network security environment and more diverse attack methods. Unlike traditional networks, 5G networks feature higher connection density, faster data transmission speeds, and [...] Read more.
With the development and widespread application of network information, new technologies led by 5G are emerging, resulting in an increasingly complex network security environment and more diverse attack methods. Unlike traditional networks, 5G networks feature higher connection density, faster data transmission speeds, and lower latency, which are widely applied in scenarios such as smart cities, the Internet of Things, and autonomous driving. The vast amounts of sensitive data generated by these applications become primary targets during the processes of collection and secure sharing, and unauthorized access or tampering could lead to severe data breaches and integrity issues. However, as 5G networks extensively employ encryption technologies to protect data transmission, attackers can hide malicious content within encrypted communication, rendering traditional content-based traffic detection methods ineffective for identifying malicious encrypted traffic. To address this challenge, this paper proposes a malicious encrypted traffic detection method based on reconstructive domain adaptation and adversarial hybrid neural networks. The proposed method integrates generative adversarial networks with ResNet, ResNeXt, and DenseNet to construct an adversarial hybrid neural network, aiming to tackle the challenges of encrypted traffic detection. On this basis, a reconstructive domain adaptation module is introduced to reduce the distribution discrepancy between the source domain and the target domain, thereby enhancing cross-domain detection capabilities. By preprocessing traffic data from public datasets, the proposed method is capable of extracting deep features from encrypted traffic without the need for decryption. The generator utilizes the adversarial hybrid neural network module to generate realistic malicious encrypted traffic samples, while the discriminator achieves sample classification through high-dimensional feature extraction. Additionally, the domain classifier within the reconstructive domain adaptation module further improves the model’s stability and generalization across different network environments and time periods. Experimental results demonstrate that the proposed method significantly improves the accuracy and efficiency of malicious encrypted traffic detection in 5G network environments, effectively enhancing the detection performance of malicious traffic in 5G networks. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems, Volume II)
►▼ Show Figures

Figure 1

21 pages, 1063 KiB  
Article
Multi-Stage Dual-Perturbation Attack Targeting Transductive SVMs and the Corresponding Adversarial Training Defense Mechanism
by Li Liu, Haiyan Chen, Changchun Yin and Yirui Fu
Electronics 2024, 13(24), 4984; https://doi.org/10.3390/electronics13244984 - 18 Dec 2024
Cited by 1 | Viewed by 701
Abstract
The Transductive Support Vector Machine (TSVM) is an effective semi-supervised learning algorithm vulnerable to adversarial sample attacks. This paper proposes a new adversarial attack method called the Multi-Stage Dual-Perturbation Attack (MSDPA), specifically targeted at TSVMs. The MSDPA has two phases: initial adversarial samples [...] Read more.
The Transductive Support Vector Machine (TSVM) is an effective semi-supervised learning algorithm vulnerable to adversarial sample attacks. This paper proposes a new adversarial attack method called the Multi-Stage Dual-Perturbation Attack (MSDPA), specifically targeted at TSVMs. The MSDPA has two phases: initial adversarial samples are generated by an arbitrary range attack, and finer attacks are performed on critical features to induce the TSVM to generate false predictions. To improve the TSVM’s defense against MSDPAs, we incorporate adversarial training into the TSVM’s loss function to minimize the loss of both standard and adversarial samples during the training process. The improved TSVM loss function considers the adversarial samples’ effect and enhances the model’s adversarial robustness. Experimental results on several standard datasets show that our proposed adversarial defense-enhanced TSVM (adv-TSVM) performs better in classification accuracy and adversarial robustness than the native TSVM and other semi-supervised baseline algorithms, such as S3VM. This study provides a new solution to improve the defense capability of kernel methods in an adversarial setting. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems, Volume II)
►▼ Show Figures

Figure 1

27 pages, 6869 KiB  
Article
Secure Aggregation-Based Big Data Analysis and Power Prediction Model for Photovoltaic Systems: A Multi-Layered Approach
by Qiwei Huang and Abubaker Wahaballa
Electronics 2024, 13(24), 4869; https://doi.org/10.3390/electronics13244869 - 10 Dec 2024
Viewed by 719
Abstract
This study presents a novel approach to enhancing the security and accuracy of photovoltaic (PV) power generation predictions through secure aggregation techniques. The research focuses on key stages of the PV data lifecycle, including data collection, transmission, storage, and analysis. To safeguard against [...] Read more.
This study presents a novel approach to enhancing the security and accuracy of photovoltaic (PV) power generation predictions through secure aggregation techniques. The research focuses on key stages of the PV data lifecycle, including data collection, transmission, storage, and analysis. To safeguard against potential attacks and prevent data leakage across these critical processes, Paillier and Brakerski–Gentry–Vaikuntanathan (BGV) homomorphic encryption methods are employed. By integrating the transport layer security (TLS) protocol with edge computing during data transmission, this study not only bolsters data security but also minimizes latency and mitigates threats. Robust strategies for key management, access control, and auditing are implemented to ensure monitored and restricted access, further enhancing system security. In the analysis phase, advanced models such as Long Short-Term Memory (LSTM) networks and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) are utilized for precise time-series predictions of PV power output. The findings demonstrate the effectiveness of these methods in managing large-scale PV datasets while maintaining high prediction accuracy and strong security measures. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems, Volume II)
►▼ Show Figures

Figure 1

23 pages, 1244 KiB  
Article
Secure and Flexible Privacy-Preserving Federated Learning Based on Multi-Key Fully Homomorphic Encryption
by Jiachen Shen, Yekang Zhao, Shitao Huang and Yongjun Ren
Electronics 2024, 13(22), 4478; https://doi.org/10.3390/electronics13224478 - 14 Nov 2024
Cited by 1 | Viewed by 2289
Abstract
Federated learning avoids centralizing data in a central server by distributing the model training process across devices, thus protecting privacy to some extent. However, existing research shows that model updates (e.g., gradients or weights) exchanged during federated learning may still indirectly leak sensitive [...] Read more.
Federated learning avoids centralizing data in a central server by distributing the model training process across devices, thus protecting privacy to some extent. However, existing research shows that model updates (e.g., gradients or weights) exchanged during federated learning may still indirectly leak sensitive information about the original data. Currently, single-key homomorphic encryption methods applied in federated learning cannot solve the problem of privacy leakage that may be caused by the collusion between the participant and the federated learning server, whereas existing privacy-preserving federated learning schemes based on multi-key homomorphic encryption in semi-honest environments have deficiencies and limitations in terms of security and application conditions. To this end, this paper proposes a privacy-preserving federated learning scheme based on multi-key fully homomorphic encryption to cope with the potential risk of privacy leakage in traditional federated learning. We designed a multi-key fully homomorphic encryption scheme, mMFHE, that encrypts by aggregating public keys and requires all participants to jointly participate in decryption sharing, thus ensuring data security and privacy. The proposed privacy-preserving federated learning scheme encrypts the model updates through multi-key fully homomorphic encryption, ensuring confidentiality under the CRS model and in a semi-honest environment. As a fully homomorphic encryption scheme, mMFHE supports homomorphic addition and homomorphic multiplication for more flexible applications. Our security analysis proves that the scheme can withstand collusive attacks by up to N−1 users and servers, where N is the total number of users. Performance analysis and experimental results show that our scheme reduces the complexity of the NAND gate, which reduces the computational load and improves the efficiency while ensuring the accuracy of the model. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems, Volume II)
►▼ Show Figures

Figure 1

26 pages, 7294 KiB  
Article
Public Authentic-Replica Sampling Mechanism in Distributed Storage Environments
by Jiale Ye, Yongmei Bai, Jiang Xu, Shitao Huang, Zhaoyang Han and Wei Wan
Electronics 2024, 13(21), 4167; https://doi.org/10.3390/electronics13214167 - 23 Oct 2024
Cited by 1 | Viewed by 1055
Abstract
With the rapid development of wireless communication and big data analysis technologies, the storage of massive amounts of data relies on third-party trusted storage, such as cloud storage. However, once data are stored on third-party servers, data owners lose physical control over their [...] Read more.
With the rapid development of wireless communication and big data analysis technologies, the storage of massive amounts of data relies on third-party trusted storage, such as cloud storage. However, once data are stored on third-party servers, data owners lose physical control over their data, making it challenging to ensure data integrity and security. To address this issue, researchers have proposed integrity auditing mechanisms that allow for the auditing of data integrity on cloud servers without retrieving all the data. To further enhance the availability of data stored on cloud servers, multiple replicas of the original data are stored on the server. However, in existing multi-replica auditing schemes, there is a problem of server fraud, where the server does not actually store the corresponding data replicas. To tackle this issue, this paper presents a formal definition of authentic replicas along with a security model for the authentic-replica sampling mechanism. Based on time-lock puzzles, identity-based encryption (IBE) mechanisms, and succinct proof techniques, we design an authentic replica auditing mechanism. This mechanism ensures the authenticity of replicas and can resist outsourcing attacks and generation attacks. Additionally, our schemes replace the combination of random numbers and replica correspondence tables with Linear Feedback Shift Registers (LFSRs), optimizing the original client-side generation and uploading of replica parameters from being linearly related to the number of replicas to a constant level. Furthermore, our schemes allow for the public recovery of replica parameters, enabling any third party to verify the replicas through these parameters. As a result, the schemes achieve public verifiability and meet the efficiency requirements for authentic-replica sampling in multi-cloud environments. This makes our scheme more suitable for distributed storage environments. The experiments show that our scheme makes the time for generating copy parameters negligible while also greatly optimizing the time required for replica generation. As the amount of replica data increases, the time spent does not grow linearly. Due to the multi-party aggregation design, the verification time is also optimal. Compared to the latest schemes, the verification time is reduced by approximately 30%. Full article
(This article belongs to the Special Issue Novel Methods Applied to Security and Privacy Problems, Volume II)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop