Towards Trustworthy AI: Techniques, Architectures, and Applications for Security and Privacy

Dear Colleagues,

The rapid advancement and widespread adoption of Artificial Intelligence (AI) have profoundly transformed application development across various domains. While offering unprecedented capabilities, this integration has introduced significant challenges in ensuring security and privacy. AI systems themselves can be vulnerable to attacks such as data poisoning, adversarial examples, and model inversion, while their deployment raises critical concerns regarding data privacy, algorithmic bias, and accountability. Building AI that is not only intelligent but also trustworthy—robust, secure, fair, and transparent—has, therefore, become an imperative for achieving sustainable and safe technological progress.

This Special Issue will compile cutting-edge research and innovative solutions dedicated to the development and implementation of trustworthy AI with a specific focus on application security and privacy. We seek contributions that address the fundamental principles, architectural designs, and practical implementations of AI systems that can defend against evolving threats, protect sensitive data, and operate reliably in real-world scenarios. Our goal is to foster discussions on holistic frameworks that integrate security and privacy into the very fabric of AI models and systems.

Topics of interest for this Special Issue include, but are not limited to, the following:

• Explainable and Transparent AI for Security Analysis;
• Adversarial Machine Learning and Robust Defense Mechanisms;
• Privacy-Preserving AI Techniques (e.g., Federated Learning, Differential Privacy);
• AI-Driven Information Hiding and Steganography;
• Secure AI Architecture and System Design;
• AI for Threat Detection and Intrusion Prevention;
• Trustworthy AI in Critical Applications (e.g., Healthcare, IoT);
• AI-Powered Authentication and Access Control;
• Security of AI Models Against Data Poisoning and Model Inversion;
• Covert Communication and Data Protection Using Generative Models;
• Detection of Steganography and Deepfakes;
• Integration of Hardware Security with AI Workflows;
• Benchmarks and Tools for Trustworthy AI Implementation.

We invite researchers, practitioners, and industry experts to submit original research articles, comprehensive reviews, and insightful case studies that contribute to the advancement of trustworthy AI in securing applications and safeguarding privacy.

Dr. Ziyang He
Dr. Yangjie Cao
Dr. Minglin Liu
Guest Editors

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