205 citations · 261 across the 7 of their papers we have counts for
8 papers
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…
A Distributed Differentially Private Algorithm for Resource Allocation in Unboundedly Large Settings
Panayiotis Danassis, Aleksei Triastcyn, Boi Faltings
We introduce a practical and scalable algorithm (PALMA) for solving one of the fundamental problems of multi-agent systems -- finding matches and allocations -- in unboundedly larg…
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…
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…
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…
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…