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Sort, Partition, Randomize: Optimal Binary Hypothesis Testing under Local Differential Privacy
For binary hypothesis testing under local differential privacy, optimal mechanisms sort inputs by likelihood ratio, partition into contiguous blocks, and apply randomized response to block labels, enabling exact O(k³)-time computation via dynamic programming.
Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026
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AI panel: 12 of 20 reviewers recommend it
lenient 3/5
medium 6/10
strict 3/5