Research map / Federated learning and privacy / Differential privacy
On Differential Private $\ell_1$, $\ell_2$ and $\ell_p^p$ Distance Queries
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Introduces a refined differentially private data structure for kernel density estimation with l1, l2 and lp^p kernels, claiming better privacy-utility tradeoff and query efficiency than prior work.
Problem
Differentially private kernel density estimation and distance queries under l1, l2 and lp^p distances over a private dataset in R^d.
Approach
A refined DP data structure for KDE with l1, l2 and lp^p kernels that improves the privacy-utility tradeoff and query efficiency over prior DP KDE results.
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