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stat.ML2020
Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees
Aleksei Triastcyn, Boi Faltings
This paper considers the problem of enhancing user privacy in common machine learning development tasks, such as data annotation and inspection, by substituting the real data with…
stat.ML2019
Federated Generative Privacy
Aleksei Triastcyn, Boi Faltings
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…