From 3a4eca3e4df386a34c5cafc14e6aed6b48acfa85 Mon Sep 17 00:00:00 2001 From: Maria Rigaki Date: Wed, 4 Nov 2020 13:36:24 +0100 Subject: [PATCH 1/2] New papers and code --- README.md | 15 ++++++++++----- 1 file changed, 10 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index f66bd91..fa9b4ee 100644 --- a/README.md +++ b/README.md @@ -2,13 +2,15 @@ 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 +- [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) - [Privacy Testing Tools](#privacy-testing-tools) - [Papers and Code](#papers-and-code) - * [Membership inference](#membership-inference) - * [Reconstruction](#reconstruction) - * [Property inference](#property-inference) - * [Model extraction](#model-extraction) + - [Membership inference](#membership-inference) + - [Reconstruction](#reconstruction) + - [Property inference](#property-inference) + - [Model extraction](#model-extraction) - [Other](#other) # 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) - [**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) +- [**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 - [**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) @@ -123,7 +127,7 @@ 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) - [**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) -- [**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) - [**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) @@ -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)) - [**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) +- [**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 - [**Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy**](https://arxiv.org/abs/2009.03561) (Naseri et al., 2020) From 1f56b7d0077d0c676cd2b43c2bf81d63a5613a8d Mon Sep 17 00:00:00 2001 From: Maria Rigaki Date: Thu, 5 Nov 2020 20:30:12 +0100 Subject: [PATCH 2/2] Added code to the NAS paper --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index fa9b4ee..ee7d7f6 100644 --- a/README.md +++ b/README.md @@ -130,7 +130,7 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib - [**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) - [**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) - [**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)