From 363a54e36497c6740dba34ef230baaa91d9943ce Mon Sep 17 00:00:00 2001 From: AndrewZhou924 Date: Sun, 18 Jun 2023 13:26:16 +0800 Subject: [PATCH] update README --- README.md | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 7320172..6281d1b 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,8 @@ If some related papers are missing, please contact us via pull requests. ### What is the model inversion attack? +A model inversion attack is a privacy attack where the attacker is able to reconstruct the original samples that were used to train the synthetic model from the generated synthetic data set. (Mostly.ai) + The goal of model inversion attacks is to recreate training data or sensitive attributes. (Chen et al, 2021.) @@ -27,6 +29,10 @@ Arxiv 2022 - Trustworthy Graph Neural Networks: Aspects, Methods and Trends. Arxiv 2022 - A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. [[paper]](https://arxiv.org/pdf/2205.10014.pdf) +Philosophical Transactions of the Royal Society A 2018. Algorithms that remember: model inversion attacks and data protection law. + +[[paper]](https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2018.0083) + ### Computer vision domain @@ -146,7 +152,7 @@ ICSE 2021 - Robustness of on-device models: Adversarial attack to deep learning [[paper]](https://arxiv.org/pdf/2101.04401) CSR Workshops 2021 - Defending Against Model Inversion Attack by Adversarial Examples. -[[paper]]https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9527945) +[[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9527945) ICML 2022 - Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks. [[paper]](https://arxiv.org/pdf/2201.12179.pdf) @@ -178,13 +184,9 @@ TIFS 2022 - Model Inversion Attack by Integration of Deep Generative Models: Pri Arxiv 2022 - Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data. [[paper]](https://arxiv.org/pdf/2205.03168.pdf) -IEEE 2021 - Defending Against Model Inversion Attack by Adversarial Examples +IEEE 2021 - Defending Against Model Inversion Attack by Adversarial Examples. [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9527945&tag=1) -ICLR 2021 - PRACTICAL DEFENCES AGAINST MODEL INVERSION ATTACKS FOR SPLIT NEURAL NETWORKS -[[paper]](https://arxiv.org/abs/2104.05743) -[[code]](https://github.com/TTitcombe/Model-Inversion-SplitNN) - ### Graph learning domain USENIX Security 2020 - Stealing Links from Graph Neural Networks.