Privacy, Security and Protection Mechanisms for Trustworthy and Resilient AI Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 May 2026 | Viewed by 8
Special Issue Editor
Interests: network security; applied cryptography; computer networks; privacy-enhancing technologies; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid integration of machine learning (ML) and large language models (LLMs) into high-stakes domains such as healthcare, finance, and critical infrastructure underscores the urgent need for privacy-preserving and security-aware AI systems. This Special Issue explores novel theoretical foundations, system architectures, and implementation strategies designed to enhance the trustworthiness and resilience of ML models. The Special Issue seeks to unite experts from academia, industry, and policymaking to foster secure, transparent, and privacy-compliant ML practices—laying the groundwork for the next generation of trustworthy and resilient AI systems.
The topics to be covered include the following:
- Privacy-preserving machine learning;
- Federated learning and decentralized AI;
- Secure aggregation and communication mechanisms;
- Privacy-enhancing technologies for ML;
- Robustness and resilience in machine learning systems;
- Defence mechanisms against poisoning and backdoor attacks;
- Adversarial attack detection and mitigation;
- Watermarking, fingerprinting, and ownership protection of ML models;
- Secure model sharing and deployment;
- Trustworthy and explainable AI under privacy constraints;
- Verification and auditability of AI pipelines;
- Ethical, legal, and regulatory aspects of secure AI.
Dr. Orhan Ermiş
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- large language models (LLMs)
- AI security and privacy
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