Information Theoretic Privacy

Information-theoretic privacy focuses on mathematically quantifying and limiting the leakage of sensitive information during data processing and analysis, aiming to guarantee privacy while preserving data utility. Current research emphasizes developing and analyzing privacy-preserving algorithms for various machine learning tasks, such as federated learning and causal inference, often employing techniques like differential privacy and secure multi-party computation within frameworks like the Privacy Funnel. This field is crucial for enabling responsible data sharing and analysis across diverse applications, including healthcare, finance, and network optimization, by providing rigorous guarantees of privacy protection.