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Creators/Authors contains: "Dharangutte, P"

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  1. ; ; ; (, The 28th International Conference on Artificial Intelligence and Statistics (AISTATS 2025))
    This work proposes a class of differentially private mechanisms for linear queries, in par- ticular range queries, that leverages corre- lated input perturbation to simultaneously achieve unbiasedness, consistency, statisti- cal transparency, and control over utility re- quirements in terms of accuracy targets ex- pressed either in certain query margins or as implied by the hierarchical database struc- ture. The proposed Cascade Sampling al- gorithm instantiates the mechanism exactly and efficiently. Our theoretical and empir- ical analysis demonstrates that we achieve near-optimal utility, effectively compete with other methods, and retain all the favorable statistical properties discussed earlier. 
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    Free, publicly-accessible full text available May 3, 2026