# Against Membership Inference Attack: Pruning is All You Need

arXiv: https://arxiv.org/abs/2008.13578 · HTML: https://mlsanity.com/paper/2008.13578/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 30 of 32)

- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [Model Sparsity Can Simplify Machine Unlearning](https://mlsanity.com/paper/2304.04934/) (2023)
- [Bag of Tricks for Training Data Extraction from Language Models](https://mlsanity.com/paper/2302.04460/) (2023)
- [Integration of Large Language Models and Federated Learning](https://mlsanity.com/paper/2307.08925/) (2023)
- [AutoReP: Automatic ReLU Replacement for Fast Private Network Inference](https://mlsanity.com/paper/2308.10134/) (2023)
- [LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference](https://mlsanity.com/paper/2309.14331/) (2023)
- [Sparse Progressive Distillation: Resolving Overfitting under Pretrain-and-Finetune Paradigm](https://mlsanity.com/paper/2110.08190/) (2021)
- [A Blessing of Dimensionality in Membership Inference through Regularization](https://mlsanity.com/paper/2205.14055/) (2022)
- [Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference](https://mlsanity.com/paper/2202.01243/) (2022)
- [Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance](https://mlsanity.com/paper/2402.01096/) (2024)
- [A Secure and Efficient Federated Learning Framework for NLP](https://mlsanity.com/paper/2201.11934/) (2022)
- [A Unified Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability](https://mlsanity.com/paper/2404.02462/) (2024)
- [Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment](https://mlsanity.com/paper/2208.05969/) (2022)
- [Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss](https://mlsanity.com/paper/2402.05453/) (2024)
- [Safety and Performance, Why Not Both? Bi-Objective Optimized Model Compression against Heterogeneous Attacks Toward AI Software Deployment](https://mlsanity.com/paper/2401.00996/) (2024)
- [On the Impact of Uncertainty and Calibration on Likelihood-Ratio Membership Inference Attacks](https://mlsanity.com/paper/2402.10686/) (2024)
- [Center-Based Relaxed Learning Against Membership Inference Attacks](https://mlsanity.com/paper/2404.17674/) (2024)
- [Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning](https://mlsanity.com/paper/2410.06814/) (2024)
- [Privacy-preserving Generative Framework Against Membership Inference Attacks](https://mlsanity.com/paper/2202.05469/) (2022)
- [Representation Magnitude has a Liability to Privacy Vulnerability](https://mlsanity.com/paper/2407.16164/) (2024)
- [CompLeak: Deep Learning Model Compression Exacerbates Privacy Leakage](https://mlsanity.com/paper/2507.16872/) (2025)
- [Differentially Private Integrated Decision Gradients (IDG-DP) for Radar-based Human Activity Recognition](https://mlsanity.com/paper/2411.02099/) (2024)
- [Can sparsity improve the privacy of neural networks?](https://mlsanity.com/paper/2304.07234/) (2023)
- [Sparsity in neural networks can improve their privacy](https://mlsanity.com/paper/2304.10553/) (2023)
- [Data Poisoning and Leakage Analysis in Federated Learning](https://mlsanity.com/paper/2409.13004/) (2024)
- [A Unified and Scalable Membership Inference Method for Visual Self-supervised Encoder via Part-aware Capability](https://mlsanity.com/paper/2505.10351/) (2025)
- [Spurious Privacy Leakage in Neural Networks](https://mlsanity.com/paper/2505.20095/) (2025)
- [SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation](https://mlsanity.com/paper/2506.19360/) (2025)
- [SoK: Challenges in Tabular Membership Inference Attacks](https://mlsanity.com/paper/2601.15874/) (2026)
- [Decoupling Generalizability and Membership Privacy Risks in Neural Networks](https://mlsanity.com/paper/2602.02296/) (2026)

## Abstract (arXiv)

The large model size, high computational operations, and vulnerability against membership inference attack (MIA) have impeded deep learning or deep neural networks (DNNs) popularity, especially on mobile devices. To address the challenge, we envision that the weight pruning technique will help DNNs against MIA while reducing model storage and computational operation. In this work, we propose a pruning algorithm, and we show that the proposed algorithm can find a subnetwork that can prevent privacy leakage from MIA and achieves competitive accuracy with the original DNNs. We also verify our theoretical insights with experiments. Our experimental results illustrate that the attack accuracy using model compression is up to 13.6% and 10% lower than that of the baseline and Min-Max game, accordingly.