Exposed! A survey of attacks on private data

Cynthia Dwork, Adam Smith, Thomas Steinke, Jonathan Ullman

Research output: Contribution to journalReview articlepeer-review

30 Scopus citations

Abstract

Privacy-preserving statistical data analysis addresses the general question of protecting privacy when publicly releasing information about a sensitive dataset. A privacy attack takes seemingly innocuous released information and uses it to discern the private details of individuals, thus demonstrating that such information compromises privacy. For example, re-identification attacks have shown that it is easy to link supposedly de-identified records to the identity of the individual concerned. This survey focuses on attacking aggregate data, such as statistics about how many individuals have a certain disease, genetic trait, or combination thereof. We consider two types of attacks: reconstruction attacks, which approximately determine a sensitive feature of all the individuals covered by the dataset, and tracing attacks, which determine whether or not a target individual's data are included in the dataset.Wealso discuss techniques from the differential privacy literature for releasing approximate aggregate statistics while provably thwarting any privacy attack.

Original languageEnglish (US)
Pages (from-to)61-84
Number of pages24
JournalAnnual Review of Statistics and Its Application
Volume4
DOIs
StatePublished - Mar 7 2017

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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