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Conference Papers Year : 2023

Differential Privacy has Bounded Impact on Fairness in Classification

Abstract

We theoretically study the impact of differential privacy on fairness in classification. We prove that, given a class of models, popular group fairness measures are pointwise Lipschitz-continuous with respect to the parameters of the model. This result is a consequence of a more general statement on accuracy conditioned on an arbitrary event (such as membership to a sensitive group), which may be of independent interest. We use this Lipschitz property to prove a non-asymptotic bound showing that, as the number of samples increases, the fairness level of private models gets closer to the one of their non-private counterparts. This bound also highlights the importance of the confidence margin of a model on the disparate impact of differential privacy.
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Dates and versions

hal-03902203 , version 1 (27-01-2023)
hal-03902203 , version 2 (18-09-2023)

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Paul Mangold, Michaël Perrot, Aurélien Bellet, Marc Tommasi. Differential Privacy has Bounded Impact on Fairness in Classification. International Conference on Machine Learning, Jul 2023, Honolulu, United States. ⟨hal-03902203v2⟩
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