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Electronics
Special Issues
Recent Advances in Cybersecurity and Information Security
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Recent Advances in Cybersecurity and Information Security

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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1982

Special Issue Editor

Prof. Dr. Chuan Zhao

E-Mail Website
Guest Editor
School of Information Science and Engineering, University of Jinan, Jinan 250022, China
Interests: secure multi-party computation; applied cryptography; privacy-preserving techniques

Special Issue Information

Dear Colleagues,

In recent years, with the rapid advancement of computer technologies such as big data, cloud computing, and artificial intelligence, there has been an increasing emphasis on cybersecurity and information security from governments, organizations, and individuals. New technologies and methodologies oriented towards security and privacy are continuously being proposed, leading to substantial progress in the fields of cybersecurity and information security to safeguard critical computing infrastructures and data privacy in the information age. The scope of this Special Issue entitled “Recent Advances in Cybersecurity and Information Security” covers the theory, applications, and implementations of cybersecurity and information security. This Special Issue aims to showcase the latest research, developments, and advances in cybersecurity and information security.

This Special Issue seeks the latest manuscripts systematically covering essential aspects of security from the latest research results of cyberspace security and information security to the real-world deployment of security technologies. Survey papers giving the state of the art in these topics are also welcome.

Potential topics include, but are not limited to, the following:

  • Cybersecurity data analytic;
  • Cryptography and its applications;
  • Operating system, database, and computing infrastructure security;
  • Software and system security;
  • Privacy and data protection;
  • Secure multi-party computation;
  • Privacy-preserving machine learning;
  • Post-quantum cryptography.

I look forward to receiving your contributions.

Prof. Dr. Chuan Zhao
Guest Editor

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

  • cyberspace security
  • information security
  • secure systems
  • mobile security
  • data privacy
  • security and privacy applications
  • cryptography

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

Published Papers (3 papers)

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Research

19 pages, 2532 KiB  
Article
Achieving High Efficiency in Schnorr-Based Multi-Signature Applications in Blockchain
by Peng Zhang, Fa Ge, Zujie Tang and Weixin Xie
Electronics 2025, 14(9), 1883; https://doi.org/10.3390/electronics14091883 - 6 May 2025
Viewed by 154
Abstract
Multi-signature applications allow multiple signers to collaboratively generate a single signature on the same message, which is widely applied in blockchain to reduce the percentage of signatures in blocks and improve the throughput of transactions. The k-sum attacks are one of the [...] Read more.
Multi-signature applications allow multiple signers to collaboratively generate a single signature on the same message, which is widely applied in blockchain to reduce the percentage of signatures in blocks and improve the throughput of transactions. The k-sum attacks are one of the major challenges in designing secure multi-signature schemes. In this work, we address k-sum attacks from a novel angle by defining a Public Third Party (PTP), which is an automatic process that can be verifiable by the public and restricts the signing phase from continuing until receiving commitments from all signers. Further, a two-round multi-signature scheme HEMS with PTP is proposed, which is secure based on the discrete logarithm assumption in the random oracle model. As each signer communicates directly with the PTP instead of other co-signers, the total amount of communication is significantly reduced. In addition, as PTP participates in the computation of the aggregation and signing algorithms, the computation cost left for each signer and verifier remains the same as the basis Schnorr signature. To the best of our knowledge, this is the high efficiency that a Schnorr-based multi-signature scheme can achieve. Further, HEMS is applied in a blockchain platform, e.g., Fabric, to improve transaction efficiency. Full article
(This article belongs to the Special Issue Recent Advances in Cybersecurity and Information Security)
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22 pages, 1400 KiB  
Article
Research on Data Ownership and Controllable Sharing Schemes in the Process of Logistics Data Flow
by Ziqi Liu, Zhanling Shi, Wenjing Wang, Rui Kong, Deqian Fu and Jianlong Qiu
Electronics 2025, 14(9), 1714; https://doi.org/10.3390/electronics14091714 - 23 Apr 2025
Viewed by 159
Abstract
The secure and effective dissemination of logistics data is frequently obstructed by issues pertaining to ownership verification and unwanted access during data exchange. This study introduces an innovative strategy for data ownership verification and regulated sharing based on the notion of “three rights [...] Read more.
The secure and effective dissemination of logistics data is frequently obstructed by issues pertaining to ownership verification and unwanted access during data exchange. This study introduces an innovative strategy for data ownership verification and regulated sharing based on the notion of “three rights separation”, which delineates data ownership into control rights, usage rights, and management rights. The proposed approach incorporates chameleon signatures and blockchain technology to facilitate dynamic, non-falsifiable ownership marking and verification. Additionally, it utilizes searchable encryption and proxy re-encryption methods to guarantee that data are viewed solely under allowed circumstances, thereby averting misuse by third-party data administrators. Security analysis verifies resilience against adaptive chosen-message assaults and keyword-guessing threats, while simulation studies illustrate the scheme’s robustness, efficiency, and practical applicability in real-world logistical settings. This framework offers a scalable and safe solution for multi-party data sharing with explicitly defined ownership control. Full article
(This article belongs to the Special Issue Recent Advances in Cybersecurity and Information Security)
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12 pages, 2453 KiB  
Article
Threshold Filtering for Detecting Label Inference Attacks in Vertical Federated Learning
by Liansheng Ding, Haibin Bao, Qingzhe Lv, Feng Zhang, Zhouyang Zhang, Jianliang Han and Shuang Ding
Electronics 2024, 13(22), 4376; https://doi.org/10.3390/electronics13224376 - 8 Nov 2024
Viewed by 1157
Abstract
Federated learning, as an emerging machine-learning method, has received widespread attention because it allows users to train locally during the training process and uses relevant cryptographic knowledge to safeguard the privacy of data during model aggregation. However, existing federated learning is also susceptible [...] Read more.
Federated learning, as an emerging machine-learning method, has received widespread attention because it allows users to train locally during the training process and uses relevant cryptographic knowledge to safeguard the privacy of data during model aggregation. However, existing federated learning is also susceptible to privacy breaches, e.g., label inference attacks against vertical federated learning scenarios, where an adversary is able to reason about the labels of other participants based on the trained model, leading to serious privacy breaches. In this paper, we design a detection method for label inference attacks in vertical federated learning scenarios, which is able to detect the attacks based on the principles of the attacks. We design a threshold-filtering detection method based on the principle of attack to determine that the model is under attack when the threshold value is greater than a set parameter. Furthermore, we have created six threat model classifications based on different a priori conditions of the adversary to comprehensively analyze the adversary’s attacks. In addition to the detection method of attacks, the extent of attacks on the model and the effectiveness of the defense can also be evaluated. The evaluation module will experimentally measure the changes in the relevant metrics such as the accuracy of the attack, the F1 score, and the change in the accuracy after the defense method. For example, detection in the full connected neural network model assesses the attack and defense effectiveness of the model with an attack accuracy of 86.72% in the breast cancer Wisconsin dataset and an F1 score of 0.743, which is reduced to 36.36% after dispersed training. This ensures that users have an overall grasp of the extent to which the training model is under attack before deploying the model. Full article
(This article belongs to the Special Issue Recent Advances in Cybersecurity and Information Security)
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