Membership Inference Attacks Against Medical Databases

Tianxiang Xu, Chang Liu, Kun Zhang, Jianlin Zhang

Abstract: Membership inference is a powerful attack to privacy databases especially for medical data. Existing attack model utilizes the shadow model to inference the private members in the privacy data-sets and information, which can damage the benefits of the data owners and may cause serious data leakage. However, existing defence are concentrated on the encryption methods, which ignore the inference can also cause the unacceptable loss in the real applications. In this work, we propose a novel inference attack model, which utilizes a shadow model to simulate the division system in the medical database and subsequently infer the members in the medical databases. Moreover, the established shadow inference model can classify the labels of medical data and obtain the privacy members in the medical databases. In contrast with traditional inference attacks, we apply the attack in the medical databases rather than recommendation system or machine learning classifiers. From our extensive simulation and comparison with traditional inference attacks, we can observe the proposed model can achieve the attacks in the medical data with reasonable attack accuracy and acceptable computation costs.