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FOCS
2010
IEEE

A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis

9 years 11 months ago
A Multiplicative Weights Mechanism for Privacy-Preserving Data Analysis
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 efficient (in terms of the runtime's dependence on the data universe size). The error is asymptotically 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 linear in the size of the data universe. As a further contribution, when we relax the utility requirement and require accuracy only for databas...
Moritz Hardt, Guy N. Rothblum
Added 11 Feb 2011
Updated 11 Feb 2011
Type Journal
Year 2010
Where FOCS
Authors Moritz Hardt, Guy N. Rothblum
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