Finding ϵ and δ of Statistical Disclosure Control Systems

Saswat Das, Keyu Zhu, Christine Task, Pascal Van Hentenryck, Ferdinando Fioretto

Research output: Contribution to journalConference Articlepeer-review

Abstract

This paper analyzes the privacy of traditional Statistical Disclosure Control (SDC) systems under a differential privacy interpretation. SDCs, such as cell suppression and swapping, promise to safeguard the confidentiality of data and are routinely adopted in data analyses with profound societal and economic impacts. Through a formal analysis and empirical evaluation on demographic data from real household in the U.S., the paper shows that widely adopted SDC systems not only induce vastly larger privacy losses than classical differential privacy mechanisms, but, they may also come at a cost of larger accuracy and fairness.

Original languageEnglish (US)
Pages (from-to)22013-22020
Number of pages8
JournalProceedings of the AAAI Conference on Artificial Intelligence
Volume38
Issue number20
DOIs
StatePublished - Mar 25 2024
Event38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada
Duration: Feb 20 2024Feb 27 2024

ASJC Scopus subject areas

  • Artificial Intelligence

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