Learning Privacy Preserving Encodings through Adversarial Training
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We present a framework to learn privacy-preserving encodings of images (or other high-dimensional data) to inhibit inference of a chosen private attribute. Rather than encoding a fixed dataset or inhibiting a fixed estimator, we aim to to learn an encoding function such that even after this function is fixed, an estimator with knowledge of the encoding is unable to learn to accurately predict the private attribute, when generalizing beyond a training set. We formulate this as adversarial optimization of an encoding function against a classifier for the private attribute, with both modeled as deep neural networks. We describe an optimization approach which successfully yields an encoder that permanently limits inference of the private attribute, while preserving either a generic notion of information, or the estimation of a different, desired, attribute. We experimentally validate the efficacy of our approach on private tasks of real-world complexity, by learning to prevent detection of scene classes from the Places-365 dataset.
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