Theory of Cryptography

Volume 3876 of the series Lecture Notes in Computer Science pp 265-284

Calibrating Noise to Sensitivity in Private Data Analysis

  • Cynthia DworkAffiliated withMicrosoft Research
  • , Frank McSherryAffiliated withMicrosoft Research
  • , Kobbi NissimAffiliated withBen-Gurion University
  • , Adam SmithAffiliated withWeizmann Institute of Science


We continue a line of research initiated in [10,11]on privacy-preserving statistical databases. Consider a trusted server that holds a database of sensitive information. Given a query function f mapping databases to reals, the so-called true answer is the result of applying f to the database. To protect privacy, the true answer is perturbed by the addition of random noise generated according to a carefully chosen distribution, and this response, the true answer plus noise, is returned to the user.

Previous work focused on the case of noisy sums, in which f = ∑ i g(x i ), where x i denotes the ith row of the database and g maps database rows to [0,1]. We extend the study to general functions f, proving that privacy can be preserved by calibrating the standard deviation of the noise according to the sensitivity of the function f. Roughly speaking, this is the amount that any single argument to f can change its output. The new analysis shows that for several particular applications substantially less noise is needed than was previously understood to be the case.

The first step is a very clean characterization of privacy in terms of indistinguishability of transcripts. Additionally, we obtain separation results showing the increased value of interactive sanitization mechanisms over non-interactive.