Publication

Federated Generative Privacy

Boi Faltings, Aleksei Triastcyn
2020
Journal Articles

Abstract

We propose FedGP, a framework for privacy-preserving data release in the federated learning setting. We use generative adversarial networks, generator components of which are trained by FedAvg algorithm, to draw private artificial data samples and empirically assess the risk of information disclosure. Our experiments show that FedGP is able to generate labeled data of high quality to successfully train and validate supervised models. Finally, we demonstrate that our approach significantly reduces vulnerability of such models to model inversion attacks.

Official source

https://infoscience.epfl.ch/entities/publication/0574a136-fdb5-4d01-ad0a-927125ea54f6

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Boi Faltings, Aleksei Triastcyn
2020
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/0574a136-fdb5-4d01-ad0a-927125ea54f6

About this result

Related concepts (17)

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Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems in industrial applications. To understand, note that most machine learning techniques are mostly designed to work on specific problem sets, under the assumption that the training and test data are generated from the same statistical distribution (IID).

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