Skip to Main content Skip to Navigation
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.
Complete list of metadata

https://hal-centralesupelec.archives-ouvertes.fr/hal-03351090
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

File

Template.pdf
Files produced by the author(s)

Licence

Public Domain

Identifiers

Citation

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⟩

Share

Metrics

Record views

30

Files downloads

41