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technical paper

AAAI 2024

February 25, 2024

Vancouver , Canada

Finding $\epsilon$ and $\delta$ of Traditional Disclosure Control Systems

keywords:

societal impact of ai

fairness

privacy

differential privacy

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 of demographic data from real households 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.

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