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New Efficient Attacks on Statistical Disclosure Control Mechanisms
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New Efficient Attacks on Statistical Disclosure Control Mechanisms
Cynthia Dwork1  and Sergey Yekhanin1
Abstract
The goal of a statistical database is to provide statistics about a population while simultaneously protecting the privacy
of the individual records in the database. The tension between privacy and usability of statistical databases has attracted
much attention in statistics, theoretical computer science, security, and database communities in recent years. A line of
research initiated by Dinur and Nissim investigates for a particular type of queries, lower bounds on the distortion needed
in order to prevent gross violations of privacy. The first result in the current paper simplifies and sharpens the Dinur and
Nissim result.
The Dinur-Nissim style results are strong because they demonstrate insecurity of all low-distortion privacy mechanisms. The
attacks have an all-or-nothing flavor: letting n denote the size of the database, Ω(n) queries are made before anything is learned, at which point Θ(n) secret bits are revealed. Restricting attention to a wide and realistic subset of possible low-distortion mechanisms, our
second result is a more acute attack, requiring only a fixed number of queries for each bit revealed.
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