Topic Editors

School of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China
Department of Computer Science, College of Engineering, Virginia Commonwealth University, Richmond, VA, USA
Faculty of Science and Technology, Charles Darwin University, Darwin 0812, Australia

Recent Advances in Security, Privacy, and Trust

Abstract submission deadline
31 October 2025
Manuscript submission deadline
31 December 2025
Viewed by
3256

Topic Information

Dear Colleagues,

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

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

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

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

Keywords

  • security
  • privacy
  • trust
  • cyberspace security
  • cryptography

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Electronics
electronics
2.9 4.7 2012 15.6 Days CHF 2400 Submit
Journal of Cybersecurity and Privacy
jcp
- - 2021 23.5 Days CHF 1000 Submit
Mathematics
mathematics
2.4 3.5 2013 16.9 Days CHF 2600 Submit
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400 Submit
Remote Sensing
remotesensing
5.0 7.9 2009 23 Days CHF 2700 Submit

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Published Papers (2 papers)

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13 pages, 4658 KiB  
Article
A Sampling-Based Method for Detecting Data Poisoning Attacks in Recommendation Systems
Mathematics 2024, 12(2), 247; https://doi.org/10.3390/math12020247 - 12 Jan 2024
Viewed by 526
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, 2119 KiB  
Article
A Certificateless Online/Offline Aggregate Signcryption Scheme against Collusion Attacks Based on Fog Computing
Electronics 2023, 12(23), 4747; https://doi.org/10.3390/electronics12234747 - 23 Nov 2023
Viewed by 549
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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