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Differential Privacy with Compression

Zhou, Shuheng and Ligett, Katrina and Wasserman, Larry (2009) Differential Privacy with Compression. . (Unpublished) http://resolver.caltech.edu/CaltechAUTHORS:20190702-110751042

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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.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/0901.1365arXivDiscussion Paper
ORCID:
AuthorORCID
Ligett, Katrina0000-0003-2780-6656
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.
Funders:
Funding AgencyGrant Number
NSF Graduate Research FellowshipUNSPECIFIED
NSFCCF-0625879
GoogleUNSPECIFIED
Carnegie Mellon UniversityUNSPECIFIED
Record Number:CaltechAUTHORS:20190702-110751042
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190702-110751042
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96884
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:08 Jul 2019 17:49
Last Modified:08 Jul 2019 17:49

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