3 citations · 5 across the 4 of their papers we have counts for
5 papers
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…
Secure and Privacy-Preserving Federated Learning via Co-Utility
Josep Domingo-Ferrer, Alberto Blanco-Justicia, Jesús Manjón +1
The decentralized nature of federated learning, that often leverages the power of edge devices, makes it vulnerable to attacks against privacy and security. The privacy risk for a…
Achieving Security and Privacy in Federated Learning Systems: Survey, Research Challenges and Future Directions
Alberto Blanco-Justicia, Josep Domingo-Ferrer, Sergio Martínez +3
Federated learning (FL) allows a server to learn a machine learning (ML) model across multiple decentralized clients that privately store their own training data. In contrast with…
The Limits of Differential Privacy (and its Misuse in Data Release and Machine Learning)
Josep Domingo-Ferrer, David Sánchez, Alberto Blanco-Justicia
Differential privacy (DP) is a neat privacy definition that can co-exist with certain well-defined data uses in the context of interactive queries. However, DP is neither a silver…
Flexible and Robust Privacy-Preserving Implicit Authentication
Josep Domingo-Ferrer, Qianhong Wu, Alberto Blanco-Justicia
Implicit authentication consists of a server authenticating a user based on the user's usage profile, instead of/in addition to relying on something the user explicitly knows (pass…