Differential privacy with compression

Shuheng Zhou*, Katrina Ligett, Larry Wasserman

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

42 Scopus citations


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. 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 [24], principal component analysis (peA), and other statistical measures [16] based on the covariance of the initial data.

Original languageAmerican English
Title of host publication2009 IEEE International Symposium on Information Theory, ISIT 2009
Number of pages5
StatePublished - 2009
Externally publishedYes
Event2009 IEEE International Symposium on Information Theory, ISIT 2009 - Seoul, Korea, Republic of
Duration: 28 Jun 20093 Jul 2009

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8102


Conference2009 IEEE International Symposium on Information Theory, ISIT 2009
Country/TerritoryKorea, Republic of


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