# Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability

arXiv: https://arxiv.org/abs/1911.09777 · HTML: https://mlsanity.com/paper/1911.09777/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 17 of 17)

- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Membership Inference Attacks and Defenses in Neural Network Pruning](https://mlsanity.com/paper/2202.03335/) (2022)
- [Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance](https://mlsanity.com/paper/2402.01096/) (2024)
- [Top Ten Challenges Towards Agentic Neural Graph Databases](https://mlsanity.com/paper/2501.14224/) (2025)
- [Knowledge Cross-Distillation for Membership Privacy](https://mlsanity.com/paper/2111.01363/) (2021)
- [Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss](https://mlsanity.com/paper/2402.05453/) (2024)
- [A Differentially Private Framework for Deep Learning with Convexified Loss Functions](https://mlsanity.com/paper/2204.01049/) (2022)
- [RAI4IoE: Responsible AI for Enabling the Internet of Energy](https://mlsanity.com/paper/2309.11691/) (2023)
- [Efficient and Private: Memorisation under differentially private parameter-efficient fine-tuning in language models](https://mlsanity.com/paper/2411.15831/) (2024)
- [Assessing the Impact of Image Dataset Features on Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2409.01329/) (2024)
- [Data Poisoning and Leakage Analysis in Federated Learning](https://mlsanity.com/paper/2409.13004/) (2024)
- [United We Defend: Collaborative Membership Inference Defenses in Federated Learning](https://mlsanity.com/paper/2601.06866/) (2026)
- [Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them](https://mlsanity.com/paper/2401.11723/) (2024)
- [Bayes-Nash Generative Privacy Against Membership Inference Attacks](https://mlsanity.com/paper/2410.07414/) (2024)
- [Privacy-Preserving Model and Preprocessing Verification for Machine Learning](https://mlsanity.com/paper/2501.08236/) (2025)
- [Privacy-Utility Trade-off in Data Publication: A Bilevel Optimization Framework with Curvature-Guided Perturbation](https://mlsanity.com/paper/2509.02048/) (2025)
- [Auditing Fairness-Privacy Trade-offs: Subpopulation-Level Effects of Fairness-Enhancing Algorithms](https://mlsanity.com/paper/2607.14607/) (2026)

## Abstract (arXiv)

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.