Efficient Algorithm for Privately Releasing Smooth Queries

Part of Advances in Neural Information Processing Systems 26 (NIPS 2013)

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Ziteng Wang, Kai Fan, Jiaqi Zhang, Liwei Wang


We study differentially private mechanisms for answering \emph{smooth} queries on databases consisting of data points in $\mathbb{R}^d$. A $K$-smooth query is specified by a function whose partial derivatives up to order $K$ are all bounded. We develop an $\epsilon$-differentially private mechanism which for the class of $K$-smooth queries has accuracy $O (\left(\frac{1}{n}\right)^{\frac{K}{2d+K}}/\epsilon)$. The mechanism first outputs a summary of the database. To obtain an answer of a query, the user runs a public evaluation algorithm which contains no information of the database. Outputting the summary runs in time $O(n^{1+\frac{d}{2d+K}})$, and the evaluation algorithm for answering a query runs in time $\tilde O (n^{\frac{d+2+\frac{2d}{K}}{2d+K}} )$. Our mechanism is based on $L_{\infty}$-approximation of (transformed) smooth functions by low degree even trigonometric polynomials with small and efficiently computable coefficients.