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@@ -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))