Differentially private data analysis of social networks via restricted sensitivity

Jeremiah Blocki, Avrim Blum, Anupam Datta, Or Sheffet

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

124 Scopus citations

Abstract

We introduce the notion of restricted sensitivity as an alternative to global and smooth sensitivity to improve accuracy in differentially private data analysis. The definition of restricted sensitivity is similar to that of global sensitivity except that instead of quantifying over all possible datasets, we take advantage of any beliefs about the dataset that a querier may have, to quantify over a restricted class of datasets. Specifically, given a query f and a hypothesis H about the structure of a dataset D, we show generically how to transform f into a new query fH whose global sensitivity (over all datasets including those that do not satisfy H) matches the restricted sensitivity of the query f. Moreover, if the belief of the querier is correct (i.e., D ∈ H) then fH(D) = f(D). If the belief is incorrect, then fH(D) may be inaccurate. We demonstrate the usefulness of this notion by considering the task of answering queries regarding social-networks, which we model as a combination of a graph and a labeling of its vertices. In particular, while our generic procedure is computationally inefficient, for the specific definition of H as graphs of bounded degree, we exhibit efficient ways of constructing fH using different projection-based techniques. We then analyze two important query classes: subgraph counting queries (e.g., number of triangles) and local profile queries (e.g., number of people who know a spy and a computer-scientist who know each other). We demonstrate that the restricted sensitivity of such queries can be significantly lower than their smooth sensitivity. Thus, using restricted sensitivity we can maintain privacy whether or not D ∈ H, while providing more accurate results in the event that H holds true.

Original languageEnglish
Title of host publicationITCS 2013 - Proceedings of the 2013 ACM Conference on Innovations in Theoretical Computer Science
Pages87-96
Number of pages10
DOIs
StatePublished - 2013
Externally publishedYes
Event2013 4th ACM Conference on Innovations in Theoretical Computer Science, ITCS 2013 - Berkeley, CA, United States
Duration: 9 Jan 201312 Jan 2013

Publication series

NameITCS 2013 - Proceedings of the 2013 ACM Conference on Innovations in Theoretical Computer Science

Conference

Conference2013 4th ACM Conference on Innovations in Theoretical Computer Science, ITCS 2013
Country/TerritoryUnited States
CityBerkeley, CA
Period9/01/1312/01/13

Keywords

  • differential privacy
  • social networks

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