3 citations · 3 across the 2 of their papers we have counts for
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cs.LG2019
Federated Learning with Bayesian Differential Privacy
Aleksei Triastcyn, Boi Faltings
We consider the problem of reinforcing federated learning with formal privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of differential privacy f…
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…
cs.LG2019
Bayesian Differential Privacy for Machine Learning
Aleksei Triastcyn, Boi Faltings
Traditional differential privacy is independent of the data distribution. However, this is not well-matched with the modern machine learning context, where models are trained on sp…