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Published July 8, 2019 | Submitted
Report Open

Differential Privacy with Compression

Abstract

This work studies formal utility and privacy guarantees for a simple multiplicative database transformation, where the data are compressed by a random linear or affine transformation, reducing the number of data records substantially, while preserving the number of original input variables. We provide an analysis framework inspired by a recent concept known as differential privacy (Dwork 06). Our goal is to show that, despite the general difficulty of achieving the differential privacy guarantee, it is possible to publish synthetic data that are useful for a number of common statistical learning applications. This includes high dimensional sparse regression (Zhou et al. 07), principal component analysis (PCA), and other statistical measures (Liu et al. 06) based on the covariance of the initial data.

Additional Information

We thank Avrim Blum and John Lafferty for helpful discussions. KL is supported in part by an NSF Graduate Research Fellowship. LW and SZ's research is supported by NSF grant CCF-0625879, a Google research grant and a grant from Carnegie Mellon's Cylab.

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Created:
August 20, 2023
Modified:
October 20, 2023