5-7 Apr 2023 Montpellier (France)

Program > Posters > Mangold Paul

Differential Privacy has Bounded Impact on Fairness in Classification
Paul Mangold  1, *@  , Michaël Perrot  1@  , Aurélien Bellet  1@  , Marc Tommasi  1@  
1 : Machine Learning in Information Networks
Inria Lille - Nord Europe, Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
* : Corresponding author

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. We use this Lipschitz property to prove a high probability bound showing that, given enough examples, the fairness level of private models is close to the one of their non-private counterparts.

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