English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

We study efficient mechanisms for differentially private kernel densityestimation (DP-KDE). Prior work for the Gaussian kernel described algorithmsthat run in time exponential in the number of dimensions $d$. This paper breaksthe exponential barrier, and shows how the KDE can privately be approximated intime linear in $d$, making it feasible for high-dimensional data. We alsopresent improved bounds for low-dimensional data.  Our results are obtained through a general framework, which we term LocalitySensitive Quantization (LSQ), for constructing private KDE mechanisms whereexisting KDE approximation techniques can be applied. It lets us leverageseveral efficient non-private KDE methods -- like Random Fourier Features, theFast Gauss Transform, and Locality Sensitive Hashing -- and ``privatize'' themin a black-box manner. Our experiments demonstrate that our resulting DP-KDEmechanisms are fast and accurate on large datasets in both high and lowdimensions.

Fast Private Kernel Density Estimation via Locality Sensitive Quantization