SoK: Comparing Different Membership Inference Attacks with a
  Comprehensive Benchmark
Jun Niu Xiaoyan Zhu Moxuan Zeng Ge Zhang Qingyang Zhao Chunhui Huang Yangming Zhang Suyu An Yangzhong Wang Xinghui Yue Zhipeng He Weihao Guo Kuo Shen Peng Liu Yulong Shen Xiaohong Jiang Jianfeng Ma Yuqing Zhang
Membership inference (MI) attacks threaten user privacy through determining
if a given data example has been used to train a target model. However, it has
been increasingly recognized that the "comparing different MI attacks"
methodology used in the existing works has serious limitations. Due to these
limitations, we found (through the experiments in this work) that some
comparison results reported in the literature are quite misleading. In this
paper, we seek to develop a comprehensive benchmark for comparing different MI
attacks, called MIBench, which consists not only the evaluation metrics, but
also the evaluation scenarios. And we design the evaluation scenarios from four
perspectives: the distance distribution of data samples in the target dataset,
the distance between data samples of the target dataset, the differential
distance between two datasets (i.e., the target dataset and a generated dataset
with only nonmembers), and the ratio of the samples that are made no inferences
by an MI attack. The evaluation metrics consist of ten typical evaluation
metrics. We have identified three principles for the proposed "comparing
different MI attacks" methodology, and we have designed and implemented the
MIBench benchmark with 84 evaluation scenarios for each dataset. In total, we
have used our benchmark to fairly and systematically compare 15
state-of-the-art MI attack algorithms across 588 evaluation scenarios, and
these evaluation scenarios cover 7 widely used datasets and 7 representative
types of models. All codes and evaluations of MIBench are publicly available at
https://github.com/MIBench/MIBench.github.io/blob/main/README.md.