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- W4383469358 abstract "We study efficient mechanisms for differentially private kernel density estimation (DP-KDE). Prior work for the Gaussian kernel described algorithms that run in time exponential in the number of dimensions $d$. This paper breaks the exponential barrier, and shows how the KDE can privately be approximated in time linear in $d$, making it feasible for high-dimensional data. We also present improved bounds for low-dimensional data. Our results are obtained through a general framework, which we term Locality Sensitive Quantization (LSQ), for constructing private KDE mechanisms where existing KDE approximation techniques can be applied. It lets us leverage several efficient non-private KDE methods -- like Random Fourier Features, the Fast Gauss Transform, and Locality Sensitive Hashing -- and ``privatize'' them in a black-box manner. Our experiments demonstrate that our resulting DP-KDE mechanisms are fast and accurate on large datasets in both high and low dimensions." @default.
- W4383469358 created "2023-07-07" @default.
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- W4383469358 date "2023-07-04" @default.
- W4383469358 modified "2023-10-18" @default.
- W4383469358 title "Fast Private Kernel Density Estimation via Locality Sensitive Quantization" @default.
- W4383469358 doi "https://doi.org/10.48550/arxiv.2307.01877" @default.
- W4383469358 hasPublicationYear "2023" @default.
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