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- W3015613036 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." @default.
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- W3015613036 date "2020-05-01" @default.
- W3015613036 modified "2023-10-18" @default.
- W3015613036 title "Learning Semi-Supervised Anonymized Representations by Mutual Information" @default.
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- W3015613036 doi "https://doi.org/10.1109/icassp40776.2020.9053379" @default.
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