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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.
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Contributor : Pablo Piantanida Connect in order to contact the contributor
Submitted on : Wednesday, January 19, 2022 - 11:06:08 PM
Last modification on : Friday, April 22, 2022 - 10:48:02 AM


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Clément Feutry, Pablo Piantanida, Pierre Duhamel. Learning Semi-Supervised Anonymized Representations by Mutual Information. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2020, Barcelona, Spain, France. pp.3467-3471, ⟨10.1109/ICASSP40776.2020.9053379⟩. ⟨hal-03351090v2⟩



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