Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability
This addresses privacy risks in machine learning for practitioners using imbalanced datasets, though it appears incremental in analyzing known vulnerabilities.
The paper investigated how data skewness affects membership inference vulnerability in machine learning models, finding that minority groups in skewed datasets face increased risk, and examined differential privacy as a mitigation technique, revealing challenging trade-offs.
Membership inference attacks seek to infer the membership of individual training instances of a privately trained model. This paper presents a membership privacy analysis and evaluation system, called MPLens, with three unique contributions. First, through MPLens, we demonstrate how membership inference attack methods can be leveraged in adversarial machine learning. Second, through MPLens, we highlight how the vulnerability of pre-trained models under membership inference attack is not uniform across all classes, particularly when the training data itself is skewed. We show that risk from membership inference attacks is routinely increased when models use skewed training data. Finally, we investigate the effectiveness of differential privacy as a mitigation technique against membership inference attacks. We discuss the trade-offs of implementing such a mitigation strategy with respect to the model complexity, the learning task complexity, the dataset complexity and the privacy parameter settings. Our empirical results reveal that (1) minority groups within skewed datasets display increased risk for membership inference and (2) differential privacy presents many challenging trade-offs as a mitigation technique to membership inference risk.