Membership Inference Attacks Against Machine Learning Models via Prediction Sensitivity
Lan Liu, Yi Wang, Gaoyang Liu, Kai Peng, Chen Wang
Abstract
Machine learning (ML) has achieved huge success in recent years, but is also vulnerable to various attacks. In this article, we concentrate on membership inference attacks and propose Aster, which merely requires the target model's black-box API and a data sample to determine whether this sample was used to train the given ML model or not. The key idea of Aster is that the training data of a fully trained ML model usually has lower prediction sensitivities compared with that of the non-training data (i.e., testing data). Less sensitivity means that when perturbing a training sample's feature value in the corresponding feature space, the prediction of the perturbed sample obtained from the target model tends to be consistent with the original prediction. In this article, we quantify the prediction sensitivity with the Jacobian matrix which could reflect the relationship between each feature's perturbation and the corresponding prediction's change. Then we regard the samples with a lower as training data. Aster can breach the membership privacy of the target model's training data with no prior knowledge about the target model or its training data. The experiment results on four datasets show that our method outperforms three state-of-the-art inference attacks.