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InfoResearchPeer-reviewed
Fundamental Limit of Discrete Distribution Estimation Under Utility-Optimized Local Differential Privacy
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Summary
This research studies discrete distribution estimation under utility-optimized local differential privacy (ULDP), which enforces LDP on sensitive data while allowing more accurate inference on non-sensitive data. The authors completely characterize the privacy-utility trade-off, proving the converse with a generalized uniform asymptotic Cramér–Rao lower bound and a reduction to extremal ULDP mechanisms. They achieve the bound with utility-optimized block design (uBD) schemes, modifications of the block design mechanism, paired with a score-based linear estimator.