Federated Generative Privacy

In this paper, 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 privacy-preserving artificial data samples and empirically assess the risk of information disclosure. Our experiments show that FedGP is able to generate labelled 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.


Published in:
Proceedings of the IJCAI Workshop on Federated Machine Learning for User Privacy and Data Confidentiality (FML 2019)
Year:
2019
Laboratories:




 Record created 2019-08-14, last modified 2019-08-19

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