Differential privacy based on importance weighting.

TitleDifferential privacy based on importance weighting.
Publication TypeJournal Article
Year of Publication2013
AuthorsJi, Z, Elkan, C
JournalMach Learn
Volume93
Issue1
Pagination163-183
Date Published2013 Oct
ISSN0885-6125
iDASH CategoryPrivacy Technology
AbstractThis paper analyzes a novel method for publishing data while still protecting privacy. The method is based on computing weights that make an existing dataset, for which there are no confidentiality issues, analogous to the dataset that must be kept private. The existing dataset may be genuine but public already, or it may be synthetic. The weights are importance sampling weights, but to protect privacy, they are regularized and have noise added. The weights allow statistical queries to be answered approximately while provably guaranteeing differential privacy. We derive an expression for the asymptotic variance of the approximate answers. Experiments show that the new mechanism performs well even when the privacy budget is small, and when the public and private datasets are drawn from different populations.
DOI10.1007/s10994-013-5396-x
Alternate JournalMach Learn
PubMed ID24482559

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