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- W3172169199 abstract "Being able to efficiently and accurately select the top-$k$ elements with differential privacy is an integral component of various private data analysis tasks. In this paper, we present the oneshot Laplace mechanism, which generalizes the well-known Report Noisy Max mechanism to reporting noisy top-$k$ elements. We show that the oneshot Laplace mechanism with a noise level of $widetilde{O}(sqrt{k}/eps)$ is approximately differentially private. Compared to the previous peeling approach of running Report Noisy Max $k$ times, the oneshot Laplace mechanism only adds noises and computes the top $k$ elements once, hence much more efficient for large $k$. In addition, our proof of privacy relies on a novel coupling technique that bypasses the use of composition theorems. Finally, we present a novel application of efficient top-$k$ selection in the classical problem of ranking from pairwise comparisons." @default.
- W3172169199 created "2021-06-22" @default.
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- W3172169199 date "2021-05-17" @default.
- W3172169199 modified "2023-09-24" @default.
- W3172169199 title "Oneshot Differentially Private Top-k Selection" @default.
- W3172169199 doi "https://doi.org/10.48550/arxiv.2105.08233" @default.
- W3172169199 hasPublicationYear "2021" @default.
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