The question of fairness: our proposal for improved DP algorithms in order to strengthen equity

While differential privacy offers robust privacy protection, it can sometimes unfairly impact certain groups. Read the research co-authored by the Tumult Labs founders on how to address this problem.

Research

Summary:

‍While differential privacy offers robust privacy protection, it applies noise to statistics in a manner that can sometimes unfairly impact certain groups. Using summaries on educational funding and voter benefit distribution from the U.S. Census Bureau, we initiate a first-of-its-kind study into the impact of formally private mechanisms (based on differential privacy) on fair and equitable decision-making. We propose novel measures of fairness in the context of randomized differentially private algorithms and identify a range of causes of outcome disparities. We also explore improved algorithms to remedy the observed unfairness.

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The question of fairness: our proposal for improved DP algorithms in order to strengthen equity

While differential privacy offers robust privacy protection, it can sometimes unfairly impact certain groups. Read the research co-authored by the Tumult Labs founders on how to address this problem.

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