Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Against Membership Inference Attack: Pruning is All You Need
Authors: Yijue Wang, Chenghong Wang, Zigeng Wang, Shanglin Zhou, Hang Liu, Jinbo Bi, Caiwen Ding, Sanguthevar Rajasekaran
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | Yijue Wang1 , Chenghong Wang2 , Zigeng Wang1 , Shanglin Zhou1 , Hang Liu 3 , Jinbo Bi 1 , Caiwen Ding 1 , Sanguthevar Rajasekaran 1 1University of Connecticut 2Duke University 3Stevens Institute of Technology |
| Pseudocode | Yes | Algorithm 1 The Process of MIA-Pruning |
| Open Source Code | No | The paper does not explicitly state that the source code for the described methodology is publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We use Le Net-5 on MNIST dataset, and VGG-16, Mobile Net V2 and Res Net18 to classify CIFAR-10 and CIFAR-100 dataset. We also use Mobile Net V2 and Res Net-18 models on the Image Net dataset to show the scalability of our proposed method. |
| Dataset Splits | Yes | The empirical gain can be calculated by simply sampling data from the training set and validation set. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | No | The detailed setting of training can be found in Appendix Section 4. The detail of pruning rate settings is in Appendix Section 6. |