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Conference papers
Learning Semi-Supervised Anonymized Representations by Mutual Information
Abstract : This paper addresses the problem of removing from a set of data (here images) a given private information, while still allowing other utilities on the processed data. This is obtained by training concurrently a GAN-like discriminator and an autoencoder. The optimization of the resulting structure involves a novel surrogate of the misclassification probability of the information to remove. Several examples are given, demonstrating that a good level of privacy can be obtained on images at the cost of the introduction of very small artifacts.
https://hal-centralesupelec.archives-ouvertes.fr/hal-02572273
Contributor : Pierre Duhamel <>
Submitted on : Wednesday, May 13, 2020 - 3:39:30 PM
Last modification on : Saturday, October 3, 2020 - 4:16:26 AM
Contributor : Pierre Duhamel <>
Submitted on : Wednesday, May 13, 2020 - 3:39:30 PM
Last modification on : Saturday, October 3, 2020 - 4:16:26 AM
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