A Variational Approach to Privacy and Fairness

Authors

Image provided by Borja Rodríguez-Gálvez
Borja
Rodríguez-Gálvez
KTH Royal Institute of Technology
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Ragnar
Thobaben
KTH Royal Institute of Technology
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Mikael
Skoglund
KTH Royal Institute of Technology

Abstract

In this article, we propose a new variational approach to learn private and/or fair representations. This approach is based on the Lagrangians of a new formulation of the privacy and fairness optimization problems that we propose. In this formulation, we aim to generate representations of the data that keep a prescribed level of the relevant information that is not shared by the private or sensitive data, while minimizing the remaining information they keep. The proposed approach (i) exhibits the similarities of the privacy and fairness problems, (ii) allows us to control the trade-off between utility and privacy or fairness through the Lagrange multiplier parameter, and (iii) can be comfortably incorporated to common representation learning algorithms such as the VAE, the beta-VAE, the VIB, or the nonlinear IB.

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