On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks
Meiyi Zhu, Caili Guo, Chunyan Feng, Osvaldo Simeone · IEEE Transactions on Information Forensics and Security · 2025
In amembership inference attack(MIA), an attacker exploits the overconfidence exhibited by typical machine learning models to determine whether a specific data point was used to train a target model. In this paper, we analyze the performance of thelikelihood ratio attack(LiRA) within an information-theoretical framework that allows the investigation of the impact of thealeatoric uncertaintyin the true data generation process, of theepistemic uncertaintycaused by a limited training data set, and of thecalibration levelof the target model. We compare three different settings, in which the attacker receives decreasingly informative feedback from the target model:confidence vector(CV) disclosure, in which the output probability vector is released;true label confidence(TLC) disclosure, in which only the probability assigned to the true label is made available by the model; anddecision set(DS) disclosure, in which an adaptive prediction set is produced as in conformal prediction. We derive bounds on the advantage of an MIA adversary with the aim of offering insights into the impact of uncertainty and calibration on the effectiveness of MIAs. Simulation results demonstrate that the derived analytical bounds predict well the effectiveness of MIAs.