Implications of Data Anonymization on the Statistical Evidence of Disparity

Abstract

Research and practical development of data-anonymization techniques have proliferated in recent years. Yet, limited attention has been paid to examine the potentially disparate impact of privacy protection on underprivileged subpopulations. This study is one of the first attempts to examine the extent to which data anonymization could mask the gross statistical disparities between subpopulations in the data. We first describe two common mechanisms of data anonymization and two prevalent types of statistical evidence for disparity. Then, we develop conceptual foundation and mathematical formalism demonstrating that the two data-anonymization mechanisms have distinctive impacts on the identifiability of disparity, which also varies based on its statistical operationalization. After validating our findings with empirical evidence, we discuss the business and policy implications, highlighting the need for firms and policy makers to balance between the protection of privacy and the recognition/rectification of disparate impact. This paper was accepted by Chris Forman, information systems.