Work

Membership Inference Attacks and Differential Privacy: A Study Within the Context of Generative Models

Year: 2025

Type: article

Abstract: Membership attacks pose a major issue in terms of secure machine learning, especially in cases in which real data are sensitive. Models tend to be overconfident in predicting labels from the training set. Nevertheless, its application has traditionally been limited to supervised models, while in the case of generative models we have found that there is a lack of theoretical foundations to bring this concept into the scene. Hence, this article provides the theoretical background in the context of membership inference attacks and their relationship to generative models, including the derivation of an evaluation metric. In addition, the link between these types of attack and differential privacy is shown to be a particular case. Lastly, we empirically show through simulations the intuition and application of the concepts derived.

Source: IEEE Open Journal of the Computer Society

DOI: 10.1109/ojcs.2025.3572244

Authors: Borja Arroyo Galende, Patricia A. Apellániz, Juan Parras, Santiago Zazo, Silvia Uribe

Institutions: Information Processing and Telecommunications Center, Universidad Politécnica de Madrid

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: European Commission, HORIZON EUROPE Framework Programme