NDLI: A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis

Content Provider	IEEE Xplore Digital Library
Author	Hardt, M. Rothblum, G.N.
Copyright Year	2010
Abstract	We consider statistical data analysis in the interactive setting. In this setting a trusted curator maintains a database of sensitive information about individual participants, and releases privacy-preserving answers to queries as they arrive. Our primary contribution is a new differentially private multiplicative weights mechanism for answering a large number of interactive counting (or linear) queries that arrive online and may be adaptively chosen. This is the first mechanism with worst-case accuracy guarantees that can answer large numbers of interactive queries and is {\em efficient} (in terms of the runtime's dependence on the data universe size). The error is asymptotically \emph{optimal} in its dependence on the number of participants, and depends only logarithmically on the number of queries being answered. The running time is nearly {\em linear} in the size of the data universe. As a further contribution, when we relax the utility requirement and require accuracy only for databases drawn from a rich class of databases, we obtain exponential improvements in running time. Even in this relaxed setting we continue to guarantee privacy for {\em any} input database. Only the utility requirement is relaxed. Specifically, we show that when the input database is drawn from a {\em smooth} distribution — a distribution that does not place too much weight on any single data item — accuracy remains as above, and the running time becomes {\em poly-logarithmic} in the data universe size. The main technical contributions are the application of multiplicative weights techniques to the differential privacy setting, a new privacy analysis for the interactive setting, and a technique for reducing data dimensionality for databases drawn from smooth distributions.
Starting Page	61
Ending Page	70
File Size	415009
Page Count	10
File Format	PDF
ISBN	9781424485253
ISSN	02725428
DOI	10.1109/FOCS.2010.85
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-10-23
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Databases Privacy Accuracy Data privacy Histograms Noise Noise measurement
Content Type	Text
Resource Type	Article

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