4 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…
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…
Truthful Text Sanitization Guided by Inference Attacks
Ildikó Pilán, Benet Manzanares-Salor, David Sánchez +1
Text sanitization aims to rewrite parts of a document to prevent disclosure of personal information. The central challenge of text sanitization is to strike a balance between priva…