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@@ -9,6 +9,8 @@ If some related papers are missing, please contact us via pull requests.
### What is the model inversion attack? ### 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. The goal of model inversion attacks is to recreate training data or sensitive attributes.
(Chen et al, 2021.) (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. Arxiv 2022 - A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection.
[[paper]](https://arxiv.org/pdf/2205.10014.pdf) [[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 ### 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) [[paper]](https://arxiv.org/pdf/2101.04401)
CSR Workshops 2021 - Defending Against Model Inversion Attack by Adversarial Examples. 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. ICML 2022 - Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks.
[[paper]](https://arxiv.org/pdf/2201.12179.pdf) [[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. 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) [[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) [[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 ### Graph learning domain
USENIX Security 2020 - Stealing Links from Graph Neural Networks. USENIX Security 2020 - Stealing Links from Graph Neural Networks.