Coupled-worlds privacy: Exploiting adversarial uncertainty in statistical data privacy

Raef Bassily, Adam Groce, Jonathan Katz, Adam Davison Smith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

We propose a new framework for defining privacy in statistical databases that enables reasoning about and exploiting adversarial uncertainty about the data. Roughly, our framework requires indistinguishability of the real world in which a mechanism is computed over the real dataset, and an ideal world in which a simulator outputs some function of a "scrubbed" version of the dataset (e.g., one in which an individual user's data is removed). In each world, the underlying dataset is drawn from the same distribution in some class (specified as part of the definition), which models the adversary's uncertainty about the dataset. We argue that our framework provides meaningful guarantees in a broader range of settings as compared to previous efforts to model privacy in the presence of adversarial uncertainty. We also show that several natural, "noiseless" mechanisms satisfy our definitional framework under realistic assumptions on the distribution of the underlying data.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, FOCS 2013
Pages439-448
Number of pages10
DOIs
StatePublished - Dec 1 2013
Event2013 IEEE 54th Annual Symposium on Foundations of Computer Science, FOCS 2013 - Berkeley, CA, United States
Duration: Oct 27 2013Oct 29 2013

Publication series

NameProceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS
ISSN (Print)0272-5428

Other

Other2013 IEEE 54th Annual Symposium on Foundations of Computer Science, FOCS 2013
CountryUnited States
CityBerkeley, CA
Period10/27/1310/29/13

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

Bassily, R., Groce, A., Katz, J., & Smith, A. D. (2013). Coupled-worlds privacy: Exploiting adversarial uncertainty in statistical data privacy. In Proceedings - 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, FOCS 2013 (pp. 439-448). [6686180] (Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS). https://doi.org/10.1109/FOCS.2013.54