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Machine learning models are increasingly used in high-stakes decision-makingsystems. In such applications, a major concern is that these models sometimesdiscriminate against certain demographic groups such as individuals withcertain race, gender, or age. Another major concern in these applications isthe violation of the privacy of users. While fair learning algorithms have beendeveloped to mitigate discrimination issues, these algorithms can still leaksensitive information, such as individuals' health or financial records.Utilizing the notion of differential privacy (DP), prior works aimed atdeveloping learning algorithms that are both private and fair. However,existing algorithms for DP fair learning are either not guaranteed to convergeor require full batch of data in each iteration of the algorithm to converge.In this paper, we provide the first stochastic differentially private algorithmfor fair learning that is guaranteed to converge. Here, the term "stochastic"refers to the fact that our proposed algorithm converges even when minibatchesof data are used at each iteration (i.e. stochastic optimization). Ourframework is flexible enough to permit different fairness notions, includingdemographic parity and equalized odds. In addition, our algorithm can beapplied to non-binary classification tasks with multiple (non-binary) sensitiveattributes. As a byproduct of our convergence analysis, we provide the firstutility guarantee for a DP algorithm for solving nonconvex-strongly concavemin-max problems. Our numerical experiments show that the proposed algorithmconsistently offers significant performance gains over the state-of-the-artbaselines, and can be applied to larger scale problems with non-binarytarget/sensitive attributes.

Stochastic Differentially Private and Fair Learning