Fundamental Limit of Discrete Distribution Estimation Under Utility-Optimized Local Differential Privacy

This research paper studies how to estimate discrete distributions (collections of data categories and their frequencies) while protecting sensitive information using utility-optimized local differential privacy (ULDP, a privacy protection method that keeps data private locally while allowing more accurate results for non-sensitive information). The authors mathematically prove the fundamental limits of this privacy-utility trade-off and propose new optimal mechanisms called utility-optimized block design schemes to achieve the best possible accuracy under these privacy constraints.