A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy

Lucas Rosenblatt, Julia Stoyanovich, Christopher Musco

    Research output: Contribution to journalConference articlepeer-review


    Differentially private (DP) mechanisms have been deployed in a variety of high-impact social settings (perhaps most notably by the U.S. Census). Since all DP mechanisms involve adding noise to results of statistical queries, they are expected to impact our ability to accurately analyze and learn from data, in effect trading off privacy with utility. Alarmingly, the impact of DP on utility can vary significantly among different sub-populations. A simple way to reduce this disparity is with stratification. First compute an independent private estimate for each group in the data set (which may be the intersection of several protected classes), then, to compute estimates of global statistics, appropriately recombine these group estimates. Our main observation is that naive stratification often yields high-accuracy estimates of population-level statistics, without the need for additional privacy budget. We support this observation theoretically and empirically. Our theoretical results center on the private mean estimation problem, while our empirical results center on extensive experiments on private data synthesis to demonstrate the effectiveness of stratification on a variety of private mechanisms. Overall, we argue that this straightforward approach provides a strong baseline against which future work on reducing utility disparities of DP mechanisms should be compared.

    Original languageEnglish (US)
    Pages (from-to)21554-21562
    Number of pages9
    JournalProceedings of the AAAI Conference on Artificial Intelligence
    Issue number19
    StatePublished - Mar 25 2024
    Event38th AAAI Conference on Artificial Intelligence, AAAI 2024 - Vancouver, Canada
    Duration: Feb 20 2024Feb 27 2024

    ASJC Scopus subject areas

    • Artificial Intelligence


    Dive into the research topics of 'A Simple and Practical Method for Reducing the Disparate Impact of Differential Privacy'. Together they form a unique fingerprint.

    Cite this