Related papers: A bounded-noise mechanism for differential privacy

A bounded-noise mechanism for differential privacy

URL: http://arxiv.org/abs/2012.03817v1
Date: Mon, 7 Dec 2020 16:03:21 GMT
Title: A bounded-noise mechanism for differential privacy
Authors: Yuval Dagan, Gil Kur
Abstract summary: We output an approximation of the average $frac1nsum_i=1n vecx(i)$ of vectors $vecx(i) in [0,1]k$, while preserving the privacy with respect to any $vecx(i)$. We present an $(epsilon,delta)$-private mechanism with optimal $ell_infty$ error for most values of $delta$.
Score: 3.9596068699962323
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Answering multiple counting queries is one of the best-studied problems in differential privacy. Its goal is to output an approximation of the average $\frac{1}{n}\sum_{i=1}^n \vec{x}^{(i)}$ of vectors $\vec{x}^{(i)} \in [0,1]^k$, while preserving the privacy with respect to any $\vec{x}^{(i)}$. We present an $(\epsilon,\delta)$-private mechanism with optimal $\ell_\infty$ error for most values of $\delta$. This result settles the conjecture of Steinke and Ullman [2020] for the these values of $\delta$. Our algorithm adds independent noise of bounded magnitude to each of the $k$ coordinates, while prior solutions relied on unbounded noise such as the Laplace and Gaussian mechanisms.

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