Emerging Trends in Network Security and Applied Cryptography

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "ICT Infrastructures for Cybersecurity".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 812

Special Issue Editors


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Guest Editor
College of Computer Science and Technology, State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
Interests: trustworthy artificial intelligence; deep learning security and privacy protection; data security and privacy protection; applied cryptography and provable security; security and privacy risk evaluation

E-Mail Website
Guest Editor
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
Interests: privacy preserving; differential privacy; artificial intelligence security; membership inference attack

Special Issue Information

Dear Colleagues,

With the development of information technologies, such as Internet, big data, cloud computing, and artificial intelligence, the network has penetrated into all aspects of daily life, such as social interaction, online shopping, mobile payment, online medical services, smart home, and AI services. 

It is undeniable that the network has indeed brought us a lot of convenience, but there are also various network information security risks, such as data leakage, identity theft, network vulnerabilities, and denial of service. As the key core technology to ensure network information security, applied cryptography plays a vital role in data protection, identity verification, message authentication, and access control, etc. Therefore, through in-depth analysis of the existing network information security risks, enhancing the protection ability of applied cryptography in network information security is a crucial topic.

This Special Issue aims to cover recent advancements in the network security and privacy risk analysis, network security and privacy defense, and applied cryptography and provable security. Both review articles and original research are welcome. Typical topics include, but are not limited to, the following:

  • Network security and privacy;
  • Big data security and privacy;
  • Cloud computing security and privacy;
  • Trustworthy artificial intelligence;
  • Machine learning security and privacy;
  • Deep learning security and privacy;
  • Federated learning security and privacy;
  • Blockchain security and privacy;
  • Applied cryptography and provable security;
  • Post-quantum cryptography;
  • Privacy-enhancing computation;
  • Security and privacy risk evaluation;
  • Trusted execution environment based zero trust model;
  • Trustworthy networks management and computing;
  • Internet-of-Things security and privacy;
  • Usable security and privacy metrics.

Dr. Hai Liu
Dr. Feng Tian
Guest Editors

Manuscript Submission Information

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Keywords

  • security and privacy risk analysis
  • network security
  • privacy protection
  • applied cryptography
  • trustworthy computing

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Published Papers (1 paper)

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Research

18 pages, 354 KB  
Article
Implementation of Ring Learning-with-Errors Encryption and Brakerski–Fan–Vercauteren Fully Homomorphic Encryption Using ChatGPT
by Zhigang Chen, Xinxia Song, Liqun Chen and Hai Liu
Computers 2025, 14(10), 440; https://doi.org/10.3390/computers14100440 - 16 Oct 2025
Abstract
This paper investigates whether ChatGPT, a large language model, can assist in the implementation of lattice-based cryptography and fully homomorphic encryption algorithms, specifically the Ring Learning-with-Errors encryption scheme and the Brakerski–Fan–Vercauteren FHE scheme. To the best of our knowledge, this study represents the [...] Read more.
This paper investigates whether ChatGPT, a large language model, can assist in the implementation of lattice-based cryptography and fully homomorphic encryption algorithms, specifically the Ring Learning-with-Errors encryption scheme and the Brakerski–Fan–Vercauteren FHE scheme. To the best of our knowledge, this study represents the first systematic exploration of ChatGPT’s ability to implement these cryptographic algorithms. Fully homomorphic encryption, despite its theoretical and practical significance, poses significant challenges due to its computational complexity and efficiency requirements. This study evaluates ChatGPT’s capability as a development tool from both algorithmic and implementation perspectives. At the algorithmic level, ChatGPT demonstrates a solid understanding of the Rring Learning-with-Errors lattice encryption scheme but faces limitations in comprehending the intricate structure of the Brakerski–Fan–Vercauteren FHE scheme. At the code level, ChatGPT can generate functional C++ implementations of both encryption schemes, significantly reducing manual coding effort. However, debugging and corrections remain necessary, particularly for the more complex Brakerski–Fan–Vercauteren scheme, where additional effort is required to ensure correctness. The findings highlight ChatGPT’s potential and limitations in supporting cryptographic algorithm development, offering insights into its application for advancing implementations of complex cryptographic systems. Full article
(This article belongs to the Special Issue Emerging Trends in Network Security and Applied Cryptography)
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