Advances in Technologies for Data Privacy and Security

Dear Colleagues,

In an era of ubiquitous data generation and rapidly advancing digital ecosystems, ensuring data privacy and security is a critical concern. The rise in artificial intelligence (AI)—particularly generative AI models—has amplified both the opportunities and risks in this domain. While AI offers powerful tools for detecting threats, automating security protocols, and enhancing privacy-preserving computation, it also introduces novel vulnerabilities, such as model inversion attacks, data leakage, and the misuse of synthetic data.

This Special Issue of Applied Sciences explores cutting-edge developments at the intersection of data privacy, security, and intelligent systems. It brings together theoretical innovations and practical applications spanning cryptographic frameworks, secure and federated learning, differential privacy, homomorphic encryption, and blockchain-based approaches. Special attention is given to privacy risks and mitigation strategies in AI systems, including techniques to secure training data, interpret model behavior, and control the dissemination of generative content.

By highlighting these multidisciplinary advances, this issue aims to foster a comprehensive understanding of how to build secure, transparent, and trustworthy AI-driven technologies. It serves as a valuable resource for researchers, developers, and policymakers navigating the evolving challenges of safeguarding data in an increasingly AI-powered world.

Dr. Tanja Pavleska
Dr. Reiner Creutzburg
Guest Editors

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