A survey on membership inference attacks and defenses in Machine Learning

Membership inference (MI) attacks mainly aim to infer whether a data record was used to train a target model or not. Due to the serious privacy risks, MI attacks have been attracting a tremendous amount of attention in the research community. Hu et al. [1] conducted — to our best knowledge — the first dedicated survey study in this specific area: the survey provides a comprehensive review of the literature during the period of 2017-2021 (e.g., over 100 papers). However, due to the tremendous amount of progress (i.e., 176 papers) made in this area since 2021, the survey conducted in [1] has unfortunately already become very limited in the following two aspects: (1) Although the entire literature from year 2017 to 2021 covers 18 ways to categorize (all the proposed) MI attacks, the literature during the period of 2017-2021, which was reviewed in [1], only covered 5 ways to categorize MI attacks. With 13 ways missing, the survey conducted in [1] only covers 27% of the landscape (in terms of how to categorize MI attacks) if a retrospective view is taken. (2) Since the literature during the period of 2017-2021 only covers 27% of the landscape (in terms of how to categorize), the number of new insights (i.e., why an MI attack could succeed) behind all the proposed MI attacks has been significantly increasing since year 2021. As a result, although none of the previous work has made the insights as a main focus of their studies, we found that the various insights leveraged in the literature can be broken down into 10 groups. Without making the insights as a main focus, a survey study could fail to help researchers gain adequate intellectual depth in this area of research. In this work, we conduct a systematic study to address these limitations. In particular, in order to address the first limitation, we make the 13 newly emerged ways to categorize MI attacks as a main focus on the study. In order to address the second limitation, we provide — to our best knowledge — the first review of the various insights leveraged in the entire literature. We found that the various insights leveraged in the literature can be broken down into 10 groups. Moreover, our survey also provides a comprehensive review of the existing defenses against MI attacks, the existing applications of MI attacks, the widely used datasets (e.g., 107 new datasets), and the evaluation metrics (e.g., 20 new evaluation metrics).