Privacy and Bias Analysis of Disclosure Avoidance Systems

TL;DR

This paper develops a framework for analyzing privacy and bias in disclosure avoidance systems, proposing differentially private versions and comparing their performance to traditional methods on census data.

Contribution

It introduces differentially private adaptations of common DA mechanisms and evaluates their privacy, accuracy, and fairness, revealing traditional methods may outperform private counterparts.

Findings

Traditional differential privacy methods can be more accurate and fair than DA mechanisms.

The framework provides formal privacy bounds for DA systems.

Empirical results on US Census data demonstrate performance differences.

Abstract

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.