Approximate privacy: Foundations and quantification

Joan Feigenbaum*, Aaron D. Jaggard, Michael Schapira

*Corresponding author for this work

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

31 Scopus citations


Increasing use of computers and networks in business, government, recreation, and almost all aspects of daily life has led to a proliferation of online sensitive data about individuals and organizations. Consequently, concern about the privacy of these data has become a top priority, particularly those data that are created and used in electronic commerce. Despite many careful formulations and extensive study, there are still open questions about the feasibility of maintaining meaningful privacy in realistic networked environments. We formulate communication-complexity-based definitions, both worst-case and average-case, of a problem's privacy-approximation ratio. We use our definitions to investigate the extent to which approximate privacy is achievable in many well studied contexts: the 2ndprice Vickrey auction [20], the millionaires problem of Yao [22], the provisioning of a public good, and also set disjointness and set intersection. We present both positive and negative results and many interesting directions for future research.

Original languageAmerican English
Title of host publicationEC'10 - Proceedings of the 2010 ACM Conference on Electronic Commerce
Number of pages12
StatePublished - 2010
Externally publishedYes
Event11th ACM Conference on Electronic Commerce, EC'10 - Cambridge, MA, United States
Duration: 7 Jun 201011 Jun 2010

Publication series

NameProceedings of the ACM Conference on Electronic Commerce


Conference11th ACM Conference on Electronic Commerce, EC'10
Country/TerritoryUnited States
CityCambridge, MA


  • approximate privacy
  • bisection auction


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