5 papers
Efficient Unlearning with Privacy Guarantees
Josep Domingo-Ferrer, Najeeb Jebreel, David Sánchez
Privacy protection laws, such as the GDPR, grant individuals the right to request the forgetting of their personal data not only from databases but also from machine learning (ML)…
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…
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…