From af8f642c0fe220a21c35b7426342a7fef3895c4b Mon Sep 17 00:00:00 2001 From: Suha Sabi Hussain Date: Wed, 22 Jul 2020 22:41:03 -0400 Subject: [PATCH] Add another MIA paper --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d504210..2bb40ae 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,7 @@ This repository contains a curated list of papers related to privacy attacks aga - [**Membership inference attack against differentially private deep learning model**](http://www.tdp.cat/issues16/tdp.a289a17.pdf) (Rahman et al., 2018) - [**Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning.**](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8835245) (Nasr et al., 2019) ([code](https://github.com/privacytrustlab/ml_privacy_meter)) - [**Logan: Membership inference attacks against generative models.**](https://content.sciendo.com/downloadpdf/journals/popets/2019/1/article-p133.xml) (Hayes et al. 2019) ([code](https://github.com/jhayes14/gen_mem_inf)) +- [**Privacy Risks of Securing Machine Learning Models against Adversarial Examples**](https://arxiv.org/abs/1905.10291) (Song et al., 2019) ([code](https://github.com/inspire-group/privacy-vs-robustness)) - [**Evaluating differentially private machine learning in practice**](https://www.usenix.org/system/files/sec19-jayaraman.pdf) (Jayaraman and Evans, 2019) ([code](https://github.com/bargavj/EvaluatingDPML)) - [**Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models**](https://www.ndss-symposium.org/wp-content/uploads/2019/02/ndss2019_03A-1_Salem_paper.pdf) (Salem et al., 2019) ([code](https://github.com/AhmedSalem2/ML-Leaks)) - [**Privacy risks of securing machine learning models against adversarial examples**](https://dl.acm.org/doi/pdf/10.1145/3319535.3354211) (Song L. et al., 2019) ([code](https://github.com/inspire-group/privacy-vs-robustness)) @@ -93,4 +94,4 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib - [**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) -- [**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) \ No newline at end of file +- [**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)