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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. 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.
# Contents # Contents
- [Awesome Attacks on Machine Learning Privacy ![Awesome](https://awesome.re)](#awesome-attacks-on-machine-learning-privacy-img-srchttpsawesomerebadgesvg-altawesome)
- [Contents](#contents)
- [Surveys and Overviews](#surveys-and-overviews) - [Surveys and Overviews](#surveys-and-overviews)
- [Privacy Testing Tools](#privacy-testing-tools) - [Privacy Testing Tools](#privacy-testing-tools)
- [Papers and Code](#papers-and-code) - [Papers and Code](#papers-and-code)
* [Membership inference](#membership-inference) - [Membership inference](#membership-inference)
* [Reconstruction](#reconstruction) - [Reconstruction](#reconstruction)
* [Property inference](#property-inference) - [Property inference](#property-inference)
* [Model extraction](#model-extraction) - [Model extraction](#model-extraction)
- [Other](#other) - [Other](#other)
# Surveys and Overviews # Surveys and Overviews
@@ -99,6 +101,8 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
- [**Does AI Remember? Neural Networks and the Right to be Forgotten**](https://uwspace.uwaterloo.ca/handle/10012/15754) (Graves et al., 2020) - [**Does AI Remember? Neural Networks and the Right to be Forgotten**](https://uwspace.uwaterloo.ca/handle/10012/15754) (Graves et al., 2020)
- [**Improving Robustness to Model Inversion Attacks via Mutual Information Regularization**](https://arxiv.org/abs/2009.05241) (Wang et al., 2020) - [**Improving Robustness to Model Inversion Attacks via Mutual Information Regularization**](https://arxiv.org/abs/2009.05241) (Wang et al., 2020)
- [**SAPAG: A Self-Adaptive Privacy Attack From Gradients**](https://arxiv.org/abs/2009.06228) (Wang et al., 2020) - [**SAPAG: A Self-Adaptive Privacy Attack From Gradients**](https://arxiv.org/abs/2009.06228) (Wang et al., 2020)
- [**Theory-Oriented Deep Leakage from Gradients via Linear Equation Solver**](https://arxiv.org/abs/2010.13356) (Pan et al., 2020)
- [**Improved Techniques for Model Inversion Attacks**](https://arxiv.org/abs/2010.04092) (Chen et al., 2020)
## Property inference ## Property inference
- [**Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers**](https://dl.acm.org/doi/10.1504/IJSN.2015.071829) (Ateniese et al., 2015) - [**Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers**](https://dl.acm.org/doi/10.1504/IJSN.2015.071829) (Ateniese et al., 2015)
@@ -123,10 +127,10 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
- [**Efficiently Stealing your Machine Learning Models**](https://encrypto.de/papers/RST19.pdf) (Reith et al., 2019) - [**Efficiently Stealing your Machine Learning Models**](https://encrypto.de/papers/RST19.pdf) (Reith et al., 2019)
- [**Extraction of Complex DNN Models: Real Threat or Boogeyman?**](https://arxiv.org/pdf/1910.05429.pdf) (Atli et al., 2020) - [**Extraction of Complex DNN Models: Real Threat or Boogeyman?**](https://arxiv.org/pdf/1910.05429.pdf) (Atli et al., 2020)
- [**Stealing Neural Networks via Timing Side Channels**](https://arxiv.org/pdf/1812.11720.pdf) (Duddu et al., 2019) - [**Stealing Neural Networks via Timing Side Channels**](https://arxiv.org/pdf/1812.11720.pdf) (Duddu et al., 2019)
- [**DeepSniffer: A DNN Model Extraction Framework Based on Learning Architectural Hints**](https://dl.acm.org/doi/pdf/10.1145/3373376.3378460) (Hu et al., 2020) - [**DeepSniffer: A DNN Model Extraction Framework Based on Learning Architectural Hints**](https://dl.acm.org/doi/pdf/10.1145/3373376.3378460) (Hu et al., 2020) ([code](https://github.com/xinghu7788/DeepSniffer))
- [**CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel**](https://www.usenix.org/system/files/sec19-batina.pdf) (Batina et al., 2019) - [**CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel**](https://www.usenix.org/system/files/sec19-batina.pdf) (Batina et al., 2019)
- [**Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures**](https://www.usenix.org/conference/usenixsecurity20/presentation/yan) (Yan et al., 2020) - [**Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures**](https://www.usenix.org/conference/usenixsecurity20/presentation/yan) (Yan et al., 2020)
- [**How to 0wn NAS in Your Spare Time**](https://arxiv.org/abs/2002.06776) (Hong et al., 2020) - [**How to 0wn NAS in Your Spare Time**](https://arxiv.org/abs/2002.06776) (Hong et al., 2020) ([code](https://github.com/Sanghyun-Hong/How-to-0wn-NAS-in-Your-Spare-Time))
- [**Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks**](https://arxiv.org/abs/1810.03487) (Hong et al., 2020) - [**Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks**](https://arxiv.org/abs/1810.03487) (Hong et al., 2020)
- [**Reverse-Engineering Deep ReLU Networks**](https://proceedings.icml.cc/static/paper_files/icml/2020/1-Paper.pdf) (Rolnick and Kording, 2020) - [**Reverse-Engineering Deep ReLU Networks**](https://proceedings.icml.cc/static/paper_files/icml/2020/1-Paper.pdf) (Rolnick and Kording, 2020)
- [**Model Extraction Oriented Data Publishing with k-anonymity**](https://link.springer.com/chapter/10.1007/978-3-030-58208-1_13) (Fukuoka et al., 2020) - [**Model Extraction Oriented Data Publishing with k-anonymity**](https://link.springer.com/chapter/10.1007/978-3-030-58208-1_13) (Fukuoka et al., 2020)
@@ -136,6 +140,7 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
- [**Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks**](https://arxiv.org/abs/1906.10908) (Orekondy et al., 2019) ([code](https://github.com/tribhuvanesh/prediction-poisoning)) - [**Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks**](https://arxiv.org/abs/1906.10908) (Orekondy et al., 2019) ([code](https://github.com/tribhuvanesh/prediction-poisoning))
- [**IReEn: Iterative Reverse-Engineering of Black-Box Functions via Neural Program Synthesis**](https://arxiv.org/abs/2006.10720) (Hajipour et al., 2020) - [**IReEn: Iterative Reverse-Engineering of Black-Box Functions via Neural Program Synthesis**](https://arxiv.org/abs/2006.10720) (Hajipour et al., 2020)
- [**ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles**](https://arxiv.org/abs/2009.09560) (Yuan et al., 2020) - [**ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles**](https://arxiv.org/abs/2009.09560) (Yuan et al., 2020)
- [**Black-Box Ripper: Copying black-box models using generative evolutionary algorithms**](https://arxiv.org/abs/2010.11158) (Barbalau et al., 2020) ([code](https://github.com/antoniobarbalau/black-box-ripper))
# Other # Other
- [**Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy**](https://arxiv.org/abs/2009.03561) (Naseri et al., 2020) - [**Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy**](https://arxiv.org/abs/2009.03561) (Naseri et al., 2020)