162 citations · 883 across the 43 of their papers we have counts for
6 papers · 1 filter
GRAIMATTER Green Paper: Recommendations for disclosure control of trained Machine Learning (ML) models from Trusted Research Environments (TREs)
Emily Jefferson, James Liley, Maeve Malone +16
TREs are widely, and increasingly used to support statistical analysis of sensitive data across a range of sectors (e.g., health, police, tax and education) as they enable secure a…
Bistochastic privacy
Nicolas Ruiz, Josep Domingo-Ferrer
We introduce a new privacy model relying on bistochastic matrices, that is, matrices whose components are nonnegative and sum to 1 both row-wise and column-wise. This class of matr…
Defending against the Label-flipping Attack in Federated Learning
Najeeb Moharram Jebreel, Josep Domingo-Ferrer, David Sánchez +1
Federated learning (FL) provides autonomy and privacy by design to participating peers, who cooperatively build a machine learning (ML) model while keeping their private data in th…
A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning
Alberto Blanco-Justicia, David Sanchez, Josep Domingo-Ferrer +1
We review the use of differential privacy (DP) for privacy protection in machine learning (ML). We show that, driven by the aim of preserving the accuracy of the learned models, DP…
FL-Defender: Combating Targeted Attacks in Federated Learning
Najeeb Jebreel, Josep Domingo-Ferrer
Federated learning (FL) enables learning a global machine learning model from local data distributed among a set of participating workers. This makes it possible i) to train more a…
Enhanced Security and Privacy via Fragmented Federated Learning
Najeeb Moharram Jebreel, Josep Domingo-Ferrer, Alberto Blanco-Justicia +1
In federated learning (FL), a set of participants share updates computed on their local data with an aggregator server that combines updates into a global model. However, reconcili…