Information-Theoretic Security and Privacy

This Special Issue titled "Information-Theoretic Security and Privacy" brings together nine peer-reviewed contributions that span both fundamental theory and practical system design. The accepted papers cover a diverse range of topics, including private information retrieval, graph-based computation and analytics, differential privacy, federated and distributed learning, sequential inference, and system-level security mechanisms for emerging platforms such as the Internet of Things (IoT) and large language model (LLM) environments. Overall, the contributions highlight how advances in this area arise from the interplay between information-theoretic modeling, algorithmic design, and application-driven constraints. Taken together, the papers confirm that information-theoretic approaches remain central to understanding and designing trustworthy, efficient, and scalable data-driven systems.