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Merge pull request #16 from HongshengHu/master

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add a github repository and a paper
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2021-11-22 08:27:31 +01:00
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@@ -33,6 +33,7 @@ This repository contains a curated list of papers related to privacy attacks aga
# Papers and Code
## Membership inference
A curated list of membership inference papers (more than 100 papers) on machine learning models is available at [this repository](https://github.com/HongshengHu/membership-inference-machine-learning-literature).
- [**Membership inference attacks against machine learning models**](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7958568) (Shokri et al., 2017) ([code](https://github.com/csong27/membership-inference))
- [**Understanding membership inferences on well-generalized learning models**](https://arxiv.org/pdf/1802.04889)(Long et al., 2018)
- [**Privacy risk in machine learning: Analyzing the connection to overfitting**](https://ieeexplore.ieee.org/document/8429311), (Yeom et al., 2018) ([code](https://github.com/samuel-yeom/ml-privacy-csf18))
@@ -86,6 +87,7 @@ This repository contains a curated list of papers related to privacy attacks aga
- [**Node-Level Membership Inference Attacks Against Graph Neural Networks**](https://arxiv.org/abs/2102.05429) (He et al., 2021)
- [**Practical Blind Membership Inference Attack via Differential Comparisons**](https://arxiv.org/abs/2101.01341) (Hui et al., 2021)
- [**ADePT: Auto-encoder based Differentially Private Text Transformation**](https://arxiv.org/abs/2102.01502) (Krishna et al., 2021)
- [**Source Inference Attacks in Federated Learning**](https://arxiv.org/abs/2109.05659) (Hu et al., 2021) ([code](https://github.com/HongshengHu/source-inference-FL))
- [**The Influence of Dropout on Membership Inference in Differentially Private Models**](https://arxiv.org/abs/2103.09008) (Galinkin, 2021)
- [**Membership Inference Attack Susceptibility of Clinical Language Models**](https://arxiv.org/abs/2104.08305) (Jagannatha et al., 2021)
- [**Membership Inference Attacks on Knowledge Graphs**](https://arxiv.org/abs/2104.08273) (Wang & Sun, 2021)