4 papers · 1 filter
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…
How to Get Actual Privacy and Utility from Privacy Models: the k-Anonymity and Differential Privacy Families
Josep Domingo-Ferrer, David Sánchez
Privacy models were introduced in privacy-preserving data publishing and statistical disclosure control with the promise to end the need for costly empirical assessment of disclosu…