On the Discredibility of Membership Inference Attacks
This work highlights a critical flaw in MI attacks for data privacy auditing, indicating they are incremental in improving attack credibility but not yet reliable for real-world applications.
The paper tackles the reliability of membership inference (MI) attacks by showing they have a high false positive rate on neighboring nonmember samples of identified members, making them unreliable for practical use, such as in legal settings where they can be challenged due to this issue.
With the wide-spread application of machine learning models, it has become critical to study the potential data leakage of models trained on sensitive data. Recently, various membership inference (MI) attacks are proposed to determine if a sample was part of the training set or not. The question is whether these attacks can be reliably used in practice. We show that MI models frequently misclassify neighboring nonmember samples of a member sample as members. In other words, they have a high false positive rate on the subpopulations of the exact member samples that they can identify. We then showcase a practical application of MI attacks where this issue has a real-world repercussion. Here, MI attacks are used by an external auditor (investigator) to show to a judge/jury that an auditee unlawfully used sensitive data. Due to the high false positive rate of MI attacks on member's subpopulations, auditee challenges the credibility of the auditor by revealing the performance of the MI attacks on these subpopulations. We argue that current membership inference attacks can identify memorized subpopulations, but they cannot reliably identify which exact sample in the subpopulation was used during the training.