205 citations · 261 across the 7 of their papers we have counts for
5 papers · 1 filter
DP-REC: Private & Communication-Efficient Federated Learning
Aleksei Triastcyn, Matthias Reisser, Christos Louizos
Privacy and communication efficiency are important challenges in federated training of neural networks, and combining them is still an open problem. In this work, we develop a meth…
Unsupervised Information Obfuscation for Split Inference of Neural Networks
Mohammad Samragh, Hossein Hosseini, Aleksei Triastcyn +3
Splitting network computations between the edge device and a server enables low edge-compute inference of neural networks but might expose sensitive information about the test quer…
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…
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…
Generating Artificial Data for Private Deep Learning
Aleksei Triastcyn, Boi Faltings
In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy with respect to the original dataset. We use…