Person
Aleksei Triastcyn
This person is no longer with EPFL
Related publications (12)
Differentially private multi-agent constraint optimization
Boi Faltings, Sujit Prakash Gujar, Aleksei Triastcyn, Sankarshan Damle
Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as ...
Dordrecht2024Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees
Boi Faltings, Aleksei Triastcyn
We consider the problem of enhancing user privacy in common data analysis and machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples from a generative adversarial network. We propose employing ...
MDPI2022Differentially Private Multi-Agent Constraint Optimization
Boi Faltings, Sujit Prakash Gujar, Aleksei Triastcyn, Sankarshan Damle
Several optimization scenarios involve multiple agents that desire to protect the privacy of their preferences. There are distributed algorithms for constraint optimization that provide improved privacy protection through secure multiparty computation. How ...
ACM2022Data-Aware Privacy-Preserving Machine Learning
In this thesis, we focus on the problem of achieving practical privacy guarantees in machine learning (ML), where the classic differential privacy (DP) fails to maintain a good trade-off between user privacy and data utility. Differential privacy guarantee ...
EPFL2020Federated Generative Privacy
Boi Faltings, Aleksei Triastcyn
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 private artificial data samples and empirica ...
IEEE COMPUTER SOC2020Bayesian Differential Privacy for Machine Learning
Boi Faltings, Aleksei Triastcyn
%0 Conference Paper %T Bayesian Differential Privacy for Machine Learning %A Aleksei Triastcyn %A Boi Faltings %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E A ...
2020Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees
Boi Faltings, Aleksei Triastcyn
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 samples form a generative adversarial network. We propose employing Bayesian ...
2020Generating Artificial Data for Private Deep Learning
Boi Faltings, Aleksei Triastcyn
In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy for the original dataset. We use generative adversarial networks to draw privacy-preserving artificial data samples and d ...
2019Federated Generative Privacy
Boi Faltings, Aleksei Triastcyn
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 ...
2019Federated Learning with Bayesian Differential Privacy
Boi Faltings, Aleksei Triastcyn
We consider the problem of reinforcing federated learning with formal privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of differential privacy for similarly distributed data, to provide sharper privacy loss bounds. We ad ...
IEEE2019