2 citations · 2 across the 3 of their papers we have counts for
5 papers
A Critical Review on the Effectiveness and Privacy Threats of Membership Inference Attacks
Najeeb Jebreel, David Sánchez, Josep Domingo-Ferrer
Membership inference attacks (MIAs) aim to determine whether a data sample was included in a machine learning (ML) model's training set and have become the de facto standard for me…
Revisiting the LiRA Membership Inference Attack Under Realistic Assumptions
Najeeb Jebreel, Mona Khalil, David Sánchez +1
Membership inference attacks (MIAs) have become the standard tool for evaluating privacy leakage in machine learning (ML). Among them, the Likelihood-Ratio Attack (LiRA) is widely…
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…
How Worrying Are Privacy Attacks Against Machine Learning?
Josep Domingo-Ferrer
In several jurisdictions, the regulatory framework on the release and sharing of personal data is being extended to machine learning (ML). The implicit assumption is that disclosin…
DP2Unlearning: An Efficient and Guaranteed Unlearning Framework for LLMs
Tamim Al Mahmud, Najeeb Jebreel, Josep Domingo-Ferrer +1
Large language models (LLMs) have recently revolutionized language processing tasks but have also brought ethical and legal issues. LLMs have a tendency to memorize potentially pri…