87 citations · 195 across the 12 of their papers we have counts for
14 papers
Membership Inference Attacks Beyond Overfitting
Mona Khalil, Alberto Blanco-Justicia, Najeeb Jebreel +1
Membership inference attacks (MIAs) against machine learning (ML) models aim to determine whether a given data point was part of the model training data. These attacks may pose sig…
Digital Forgetting in Large Language Models: A Survey of Unlearning Methods
Alberto Blanco-Justicia, Najeeb Jebreel, Benet Manzanares +4
The objective of digital forgetting is, given a model with undesirable knowledge or behavior, obtain a new model where the detected issues are no longer present. The motivations fo…
An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdata
David Sánchez, Najeeb Jebreel, Krishnamurty Muralidhar +2
The threat of reconstruction attacks has led the U.S. Census Bureau (USCB) to replace in the Decennial Census 2020 the traditional statistical disclosure limitation based on rank s…
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…
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…
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…
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…
Group Discounts Compatible with Buyer Privacy
Josep Domingo-Ferrer, Alberto Blanco-Justicia
We show how group discounts can be offered without forcing buyers to surrender their anonymity, as long as buyers can use their own computing devices (e.g. smartphone, tablet or co…
Privacy-preserving Loyalty Programs
Alberto Blanco-Justicia, Josep Domingo-Ferrer
Loyalty programs are promoted by vendors to incentivize loyalty in buyers. Although such programs have become widespread, they have been criticized by business experts and consumer…
Privacy-Preserving Trust Management Mechanisms from Private Matching Schemes
Oriol Farràs, Josep Domingo-Ferrer, Alberto Blanco-Justicia
Cryptographic primitives are essential for constructing privacy-preserving communication mechanisms. There are situations in which two parties that do not know each other need to e…