Privacy Amplification in Differential Privacy: An Information-Theoretic Perspective

Abstract

Privacy amplification, grounded in information theory, plays a crucial role in strengthening the guarantees of differential privacy. By introducing carefully calibrated randomness through noise addition and data perturbation, privacy amplification techniques reduce the risk of sensitive information leakage while preserving the utility of statistical analyses. Advanced frameworks such as Rényi Differential Privacy (RDP) and Concentrated Differential Privacy (CDP) provide tighter and more flexible privacy bounds, enabling improved trade-offs between privacy and accuracy. This paper explores the theoretical foundations of privacy amplification and examines its practical applications in modern data-driven systems. By optimizing noise parameters and leveraging probabilistic transformations, privacy amplification emerges as a powerful tool for ensuring robust privacy protection in large-scale data analysis and machine learning environments.