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Disclosure avoidance (DA) systems are used to safeguard the confidentialityof data while allowing it to be analyzed and disseminated for analyticpurposes. These methods, e.g., cell suppression, swapping, and k-anonymity, arecommonly applied and may have significant societal and economic implications.However, a formal analysis of their privacy and bias guarantees has beenlacking. This paper presents a framework that addresses this gap: it proposesdifferentially private versions of these mechanisms and derives their privacybounds. In addition, the paper compares their performance with traditionaldifferential privacy mechanisms in terms of accuracy and fairness on US Censusdata release and classification tasks. The results show that, contrary topopular beliefs, traditional differential privacy techniques may be superior interms of accuracy and fairness to differential private counterparts of widelyused DA mechanisms.

Privacy and Bias Analysis of Disclosure Avoidance Systems