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# Awesome atacks on ML privacy [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
This repository contains a curated list of papers related to privacy attacks against machine learning. A code repository is provided when available. For corections, suggestions and missing papers, please either open an issue or submit a pull request.
This repository contains a curated list of papers related to privacy attacks against machine learning. A code repository is provided when available by the authors. For corrections, suggestions or missing papers, please either open an issue or submit a pull request.
## Table of Contents
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- [**Model Reconstruction from Model Explanations**](https://dl.acm.org/doi/abs/10.1145/3287560.3287562) (Milli et al., 2019)
- [**Exploring connections between active learning and model extraction**](https://www.usenix.org/system/files/sec20summer_chandrasekaran_prepub.pdf) (Chandrasekaran et al., 2020)
- [**High Accuracy and High Fidelity Extraction of Neural Networks**](https://www.usenix.org/conference/usenixsecurity20/presentation/jagielski) (Jagielski et al., 2020)
- [**Thieves on Sesame Street! Model Extraction of BERT-based APIs**](https://openreview.net/attachment?id=Byl5NREFDr&name=original_pdf) (Krishna et al., 2020) ([code](https://github.com/google-research/language/tree/master/language/bert_extraction))
- [**Thieves on Sesame Street! Model Extraction of BERT-based APIs**](https://openreview.net/attachment?id=Byl5NREFDr&name=original_pdf) (Krishna et al., 2020) ([code](https://github.com/google-research/language/tree/master/language/bert_extraction))