Optimizing linear counting queries under differential privacy

  • Li C
  • Hay M
  • Rastogi V
 et al. 
  • 54

    Readers

    Mendeley users who have this article in their library.
  • 133

    Citations

    Citations of this article.

Abstract

Differential privacy is a robust privacy standard that has been successfully applied to a range of data analysis tasks. But despite much recent work, optimal strategies for answering a collection of related queries are not known. We propose the matrix mechanism, a new algorithm for answering a workload of predicate counting queries. Given a workload, the mechanism requests answers to a different set of queries, called a query strategy, which are answered using the standard Laplace mechanism. Noisy answers to the workload queries are then derived from the noisy answers to the strategy queries. This two stage process can result in a more complex correlated noise distribution that preserves differential privacy but increases accuracy. We provide a formal analysis of the error of query answers produced by the mechanism and investigate the problem of computing the optimal query strategy in support of a given workload. We show this problem can be formulated as a rank-constrained semidefinite program. Finally, we analyze two seemingly distinct techniques, whose similar behavior is explained by viewing them as instances of the matrix mechanism.

Author-supplied keywords

  • diff-
  • erential privacy
  • output perturbation
  • private data analysis
  • semidefinite program

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text

Authors

  • Chao Li

  • Michael Hay

  • Vibhor Rastogi

  • Gerome Miklau

  • Andrew McGregor

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free