Universally utility-maximizing privacy mechanisms

A mechanism for releasing information about a statistical database with sensitive data must resolve a trade-off between utility and privacy. Publishing fully accurate information maximizes utility while minimizing privacy, while publishing random noise accomplishes the opposite. Privacy can be rigorously quantified using the framework of differential privacy, which requires that a mechanism's output distribution is nearly the same whether or not a given database row is included or excluded. The goal of this paper is strong and general utility guarantees, subject to differential privacy. We pursue mechanisms that guarantee near-optimal utility to every potential user, independent of its side information (modeled as a prior distribution over query results) and preferences (modeled via a loss function). Our main result is: for each fixed count query and differential privacy level, there is a geometric mechanism M -- a discrete variant of the simple and well-studied mechanism that ad...

• Type: Conference
• Year: 2009
• Where: STOC
• Authors: Arpita Ghosh, Tim Roughgarden, Mukund Sundararajan
• Keywords: Algorithms, Geometric Mechanism, Optimal Mechanism Mu, Private Mechanism Mu, STOC 2009