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Implications of Data Anonymization on the Statistical Evidence of Disparity

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  • Heng Xu

    (Kogod School of Business, American University, Washington, District of Columbia 20016)

  • Nan Zhang

    (Kogod School of Business, American University, Washington, District of Columbia 20016)

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.

Suggested Citation

  • Heng Xu & Nan Zhang, 2022. "Implications of Data Anonymization on the Statistical Evidence of Disparity," Management Science, INFORMS, vol. 68(4), pages 2600-2618, April.
    • Handle: RePEc:inm:ormnsc:v:68:y:2022:i:4:p:2600-2618
    DOI: 10.1287/mnsc.2021.4028
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    References listed on IDEAS

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    1. Nan Zhang & Heng Xu, 2024. "Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning," Information Systems Research, INFORMS, vol. 35(2), pages 469-488, June.

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