https://peertube.r2.enst.fr/videos/watch/0f2c9d3b-5bad-4a05-8763-c36acb8ad21a Speaker: Pablo Piantanida, Associate Professor of Information Theory at CentraleSupelec Abstract: Statistical methods protecting sensitive information or the identity of the data owner have become critical to ensure privacy of individuals as well as of organizations. In this talk, we present a statistical anonymization method based on representation learning and deep neural networks. Our approach employs adversarial networks to perform a novel variational approximation of the mutual information between the representations and the user’s identity. We introduce a training objective for simultaneously learning representations that preserve the information of interest (e.g., about regular labels) while dismissing information about the identity of a person (e.g., about private labels). We demonstrate the success of this approach for standard classification versus anonymization tasks. See others videos of the MLAI workshop on www.mlai-workshop.org Sat, 25 Jul 2026 23:28:39 GMT https://validator.w3.org/feed/docs/rss2.html PeerTube - https://peertube.r2.enst.fr https://peertube.r2.enst.fr/lazy-static/avatars/216a33fc-1ec0-4e00-aae8-030127d397f2.png https://peertube.r2.enst.fr/videos/watch/0f2c9d3b-5bad-4a05-8763-c36acb8ad21a All rights reserved, unless otherwise specified in the terms specified at https://peertube.r2.enst.fr/about and potential licenses granted by each content's rightholder.