[2603.22987] A Critical Review on the Effectiveness and Privacy Threats of Membership Inference Attacks

Computer Science > Cryptography and Security arXiv:2603.22987 (cs) [Submitted on 24 Mar 2026] Title:A Critical Review on the Effectiveness and Privacy Threats of Membership Inference Attacks Authors:Najeeb Jebreel, David Sánchez, Josep Domingo-Ferrer View a PDF of the paper titled A Critical Review on the Effectiveness and Privacy Threats of Membership Inference Attacks, by Najeeb Jebreel and 2 other authors View PDF HTML (experimental) Abstract: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 measuring privacy leakages in ML. We propose an evaluation framework that defines the conditions under which MIAs constitute a genuine privacy threat, and review representative MIAs against it. We find that, under the realistic conditions defined in our framework, MIAs represent weak privacy threats. Thus, relying on them as a privacy metric in ML can lead to an overestimation of risk and to unnecessary sacrifices in model utility as a consequence of employing too strong defenses. Comments: Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG) Cite as: arXiv:2603.22987 [cs.CR]   (or arXiv:2603.22987v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2603.22987 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Najeeb Jebreel [view email] [v1] Tue, 24 Mar 2026 09:23:57 UTC (89 KB) Full-...