Editorial: Artificial Intelligence-Driven Information Security and Privacy Enhancement Technologies

Abstract

The rapid development of digitalization and intelligent computing has reshaped the technical foundations of information systems, data services, industrial platforms, and networked infrastructures. At the same time, it has also intensified the risks associated with information security, privacy protection, and trustworthy data processing. Generative artificial intelligence, large-scale data analytics, distributed computing, cloud services, Internet of Things systems, and edge intelligence have introduced new opportunities for automated defense, intelligent monitoring, and privacy-preserving computation. However, these technologies also create new attack surfaces, increase system complexity, and challenge conventional security mechanisms that rely mainly on static rules, predefined signatures, or isolated protection models. The Special Issue on Artificial Intelligence-Driven Information Security and Privacy Enhancement Technologies focuses on recent research at the intersection of artificial intelligence, cybersecurity, privacy protection, cryptographic technologies, secure computation, and intelligent system defense. The motivation of this special issue lies in the need to develop more adaptive, verifiable, scalable, and privacy-aware security mechanisms for increasingly complex digital environments. In particular, AI-driven methods have the potential to improve malicious code detection, vulnerability discovery, fuzz testing, network attack detection, cryptographic computation, zero-knowledge verification, and privacy-preserving distributed learning. These research directions are particularly important as modern information systems must simultaneously address security effectiveness, computational efficiency, privacy compliance, and deployment feasibility. This special issue was organized as an open call and was not associated with any conference. In total, 44 submissions were received. Each accepted paper received at least three reviews. After the first round of peer review, 36 submissions were rejected. The remaining eight papers underwent a second round of review and revision, during which the authors were required to respond carefully to the reviewers’ comments. After further editorial assessment, two additional papers were rejected because of possible potential risks identified during the final review stage. As a result, six papers were finally accepted for inclusion in this special issue. We would like to thank all authors who submitted their work to this special issue. We also sincerely thank all reviewers for their time, expertise, and constructive comments, which were essential to maintaining the academic quality of the issue. We are grateful to the J.UCS editorial team for their support throughout the preparation and publication process. We hope that this special issue will provide readers with useful insights into emerging AI-driven methods for information security and privacy enhancement.  Jingsha He, Dragan Peraković & Biplob Ray