An Information-Theoretic Characterization of Pufferfish Privacy

Theshani Nuradha, Ziv Goldfeld

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

1 Scopus citations


Pufferfish privacy (PP) is an appealing generalization of differential privacy (DP), that offers flexibility in specifying sensitive information and integrating domain knowledge into the privacy definition. Inspired by the illuminating equivalent formulation of DP in terms of mutual information proposed by Cuff and Yu [1], this work explores PP through the lens of information theory. We provide an equivalent information-theoretic formulation of PP as the conditional mutual information between the mechanism and the secret, given the public information. This formulation lends well for an information-theoretic analysis, and we use it to prove convexity, composability, and post-processing properties for PP mechanisms. We also leverage our formulation to derive noise levels for the Gaussian PP mechanisms.

Original languageEnglish
Title of host publication2022 IEEE International Symposium on Information Theory, ISIT 2022
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781665421591
StatePublished - 1 Jan 2022
Externally publishedYes
Event2022 IEEE International Symposium on Information Theory, ISIT 2022 - Espoo, Finland
Duration: 26 Jun 20221 Jul 2022

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095


Conference2022 IEEE International Symposium on Information Theory, ISIT 2022

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics


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