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arXiv · 1401.0987

Differentially Private Data Releasing for Smooth Queries with Synthetic Database Output

Chi Jin·Ziteng Wang·Junliang Huang·Yiqiao Zhong·Liwei Wang
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Abstract

We consider accurately answering smooth queries while preserving differential privacy. A query is said to be $K$-smooth if it is specified by a function defined on $[-1,1]^d$ whose partial derivatives up to order $K$ are all bounded. We develop an $\epsilon$-differentially private mechanism for the class of $K$-smooth queries. The major advantage of the algorithm is that it outputs a synthetic database. In real applications, a synthetic database output is appealing. Our mechanism achieves an accuracy of $O (n^{-\frac{K}{2d+K}}/\epsilon )$, and runs in polynomial time. We also generalize the mechanism to preserve $(\epsilon, \delta)$-differential privacy with slightly improved accuracy. Extensive experiments on benchmark datasets demonstrate that the mechanisms have good accuracy and are efficient.

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Chi Jin, Ziteng Wang, Junliang Huang, Yiqiao Zhong, Liwei Wang. 2014-01-06. Differentially Private Data Releasing for Smooth Queries with Synthetic Database Output. https://arxiv.org/abs/1401.0987

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