  Disclosure avoidance (DA) systems are used to safeguard the confidentiality
of data while allowing it to be analyzed and disseminated for analytic
purposes. These methods, e.g., cell suppression, swapping, and k-anonymity, are
commonly applied and may have significant societal and economic implications.
However, a formal analysis of their privacy and bias guarantees has been
lacking. This paper presents a framework that addresses this gap: it proposes
differentially private versions of these mechanisms and derives their privacy
bounds. In addition, the paper compares their performance with traditional
differential privacy mechanisms in terms of accuracy and fairness on US Census
data release and classification tasks. The results show that, contrary to
popular beliefs, traditional differential privacy techniques may be superior in
terms of accuracy and fairness to differential private counterparts of widely
used DA mechanisms.
