Security and Privacy in Artificial Intelligence Systems

Dear Colleagues,

Artificial Intelligence (AI) has become the core engine driving innovation across diverse domains, including autonomous vehicles, healthcare, finance, and next-generation communication systems. However, the rapid deployment of AI introduces new vectors of security and privacy risks: adversarial attacks against models, data poisoning, model inversion, backdoor threats, and privacy leakage from training data. These vulnerabilities not only compromise system integrity but also raise ethical and regulatory concerns for trustworthy AI adoption.

This Special Issue will explore cutting-edge research on security and privacy in AI systems, covering theoretical foundations, algorithmic advances, and practical applications. By focusing on both attack and defense perspectives, as well as privacy-preserving AI frameworks, this Special Issue will provide a comprehensive view of how to build resilient and trustworthy AI ecosystems. Its scope is well aligned with that of Electronics, emphasizing digital technologies, system reliability, and user protection.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Adversarial attacks and defences in AI/ML models;
• Data poisoning, backdoor, and evasion attacks;
• Federated learning security and privacy;
• Differential privacy and homomorphic encryption for AI;
• Blockchain-enabled secure and trustworthy AI;
• Privacy-preserving data sharing and knowledge extraction;
• Secure and robust AI for IoT, edge, and 6G networks;
• Explainable AI (XAI) and its role in system trustworthiness;
• AI-driven intrusion detection and cyber defense.

We look forward to receiving your contributions.

Dr. Yangfei Lin
Dr. Qiong Chang
Guest Editors

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