Privacy and Bias Analysis of Disclosure Avoidance Systems
This work addresses privacy and bias issues in data dissemination for societal and economic applications, but it is incremental as it builds on existing differential privacy frameworks.
The paper tackles the lack of formal analysis of privacy and bias in disclosure avoidance systems by proposing differentially private versions and comparing them to traditional differential privacy methods on US Census data, finding that traditional differential privacy techniques are superior in accuracy and fairness.
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.