Merge pull request #2 from stratosphereips/master

Update

README.md

@@ -24,7 +24,6 @@ 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))
@@ -93,7 +92,6 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**CloudLeak: Large-Scale Deep Learning Models Stealing Through Adversarial Examples**](https://www.ndss-symposium.org/ndss-paper/cloudleak-large-scale-deep-learning-models-stealing-through-adversarial-examples/) (Yu et al., 2020) 
 - [**ACTIVETHIEF: Model Extraction Using Active Learning and Unannotated Public Data**](https://aaai.org/Papers/AAAI/2020GB/AAAI-PalS.7093.pdf) (Pal et al., 2020)
 - [**Efficiently Stealing your Machine Learning Models**](https://encrypto.de/papers/RST19.pdf) (Reith et al., 2019) 
-- [**A framework for the extraction of Deep Neural Networks by leveraging public data**](https://arxiv.org/abs/1905.09165) (Pal 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)