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Annual Review of Statistics and Its Application

Volume 4, 2017

Exposed! A Survey of Attacks on Private Data

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. We also discuss techniques from the differential privacy literature for releasing approximate aggregate statistics while provably thwarting any privacy attack.

Keyword(s): differential privacy, privacy, privacy attacks, re-identification, reconstruction attacks, tracing attacks
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/content/journals/10.1146/annurev-statistics-060116-054123
2017-03-07
2024-04-19
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/content/journals/10.1146/annurev-statistics-060116-054123
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Exposed! A Survey of Attacks on Private Data
Annual Review of Statistics and Its Application 4, 61 (2017); https://doi.org/10.1146/annurev-statistics-060116-054123
/content/journals/10.1146/annurev-statistics-060116-054123
/content/journals/10.1146/annurev-statistics-060116-054123
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  • Article Type: Review Article

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