# Membership Inference Attacks on Lottery Ticket Networks

arXiv: https://arxiv.org/abs/2108.03506 · HTML: https://mlsanity.com/paper/2108.03506/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Membership inference attacks, one of 12 directions in LLM safety and security.
- Builds on 13 of its 17 references that are in the map.
- First posted on arXiv 7 Aug 2021.

## Placed in

- LLM safety and security / [Membership inference attacks](https://mlsanity.com/safety/membership-inference-attacks/)

## Builds on (most cited first, 13 of 13)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Linear Mode Connectivity and the Lottery Ticket Hypothesis](https://mlsanity.com/paper/1912.05671/) (2019)
- [ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models](https://mlsanity.com/paper/1806.01246/) (2018)
- [Membership Inference Attacks on Machine Learning: A Survey](https://mlsanity.com/paper/2103.07853/) (2021)
- [The Lottery Ticket Hypothesis for Pre-trained BERT Networks](https://mlsanity.com/paper/2007.12223/) (2020)
- [Proving the Lottery Ticket Hypothesis: Pruning is All You Need](https://mlsanity.com/paper/2002.00585/) (2020)
- [A Unified Lottery Ticket Hypothesis for Graph Neural Networks](https://mlsanity.com/paper/2102.06790/) (2021)
- [One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers](https://mlsanity.com/paper/1906.02773/) (2019)
- [Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning](https://mlsanity.com/paper/1812.00910/) (2018)
- [Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP](https://mlsanity.com/paper/1906.02768/) (2019)
- [The Lottery Ticket Hypothesis for Object Recognition](https://mlsanity.com/paper/2012.04643/) (2020)
- [Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation](https://mlsanity.com/paper/1912.09685/) (2019)

## Built on by (most cited first, 3 of 3)

- [Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference](https://mlsanity.com/paper/2202.01243/) (2022)
- [Defending Membership Inference Attacks via Privacy-aware Sparsity Tuning](https://mlsanity.com/paper/2410.06814/) (2024)
- [Can sparsity improve the privacy of neural networks?](https://mlsanity.com/paper/2304.07234/) (2023)

## Abstract (arXiv)

The vulnerability of the Lottery Ticket Hypothesis has not been studied from the purview of Membership Inference Attacks. Through this work, we are the first to empirically show that the lottery ticket networks are equally vulnerable to membership inference attacks. A Membership Inference Attack (MIA) is the process of determining whether a data sample belongs to a training set of a trained model or not. Membership Inference Attacks could leak critical information about the training data that can be used for targeted attacks. Recent deep learning models often have very large memory footprints and a high computational cost associated with training and drawing inferences. Lottery Ticket Hypothesis is used to prune the networks to find smaller sub-networks that at least match the performance of the original model in terms of test accuracy in a similar number of iterations. We used CIFAR-10, CIFAR-100, and ImageNet datasets to perform image classification tasks and observe that the attack accuracies are similar. We also see that the attack accuracy varies directly according to the number of classes in the dataset and the sparsity of the network. We demonstrate that these attacks are transferable across models with high accuracy.