This repo is a PyTorch implementation of "Debiasing Learning for Membership Inference Attacks Against Recommender Systems" (KDD 2022).
State-of-the-art membership inference attacks against recommender systems face two challenging problems:
- training data for the attack model is biased due to the gap between shadow and target recommenders, and
- hidden states in recommenders are not observational, resulting in inaccurate estimations of difference vectors.
In this paper, the proposed DL-MIA framework aims to address above limitations. To mitigate the gap between recommenders, a VAE based disentangled encoder is devised to identify recommender invariant and specific features. To reduce the estimation bias, we design a weight estimator, assigning a truth-level score for each difference vector to indicate estimation accuracy.
We evaluate DL-MIA against both general recommenders and sequential recommenders on three real-world datasets (i.e., MovieLens-1M, Amazon Digital Music, and Amazon Beauty).
- Python 3.8
- PyTorch 1.7
- NumPy
For dataset preprocessing, please see detailed instructions in DATASET.md.
The following commands can be used to train DL-MIA for sequential recommenders:
cd DL-MIA/DL-MIA-SR/Joint-Training/SMDD/
sh ACMC.shNote that slurm configuration commands in ACMC.sh should be commented out if there is no slurm manager on your server.
To evaluate trained models, please set --is_eval in ACMC.sh to 1
@inproceedings{wang2022Debiasing,
author = {Zihan Wang, Na Huang, Fei Sun, Pengjie Ren, Zhumin Chen, Hengliang Luo, Maarten de Rijke, and Zhaochun Ren},
title = {Debiasing Learning for Membership Inference Attacks Against Recommender Systems},
booktitle = {{KDD} '22: The 28th {ACM} {SIGKDD} Conference on Knowledge Discovery and Data Mining, USA, August 14-18, 2022},
year = {2022}
}