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@@ -11,7 +11,7 @@ This repository contains a curated list of papers related to privacy attacks aga
 ## Membership inference
 - [**Membership inference attacks against machine learning models**](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7958568) (Shokri et al., 2017) ([code](https://github.com/csong27/membership-inference))
 - [**Understanding membership inferences on well-generalized learning models**](https://arxiv.org/pdf/1802.04889)(Long et al., 2018)
-- [**Privacy risk in machine learning:Analyzing the connection to overfitting**](https://ieeexplore.ieee.org/document/8429311), (Yeom et al., 2018) ([code](https://github.com/samuel-yeom/ml-privacy-csf18))
+- [**Privacy risk in machine learning: Analyzing the connection to overfitting**](https://ieeexplore.ieee.org/document/8429311), (Yeom et al., 2018) ([code](https://github.com/samuel-yeom/ml-privacy-csf18))
 - [**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))
@@ -29,6 +29,9 @@ This repository contains a curated list of papers related to privacy attacks aga
 - [**Revisiting Membership Inference Under Realistic Assumptions**](https://arxiv.org/pdf/2005.10881.pdf) (Jayaraman et al., 2020)
 - [**When Machine Unlearning Jeopardizes Privacy**](https://arxiv.org/pdf/2005.02205.pdf) (Chen et al., 2020)
 - [**Modelling and Quantifying Membership Information Leakage in Machine Learning**](https://arxiv.org/pdf/2001.10648.pdf) (Farokhi and Kaafar, 2020)
+- [**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))
+- [**Systematic Evaluation of Privacy Risks of Machine Learning Models**](https://arxiv.org/abs/2003.10595) (Song and Mittal, 2020) ([code](https://github.com/inspire-group/membership-inference-evaluation))
+- [**Towards the Infeasibility of Membership Inference on Deep Models**](https://arxiv.org/pdf/2005.13702.pdf) (Rezaei and Liu, 2020) ([code](https://github.com/shrezaei/MI-Attack))
 
 ## Reconstruction
 Reconstruction attacks cover also attacks known as *model inversion* and *attribute inference*.
@@ -56,7 +59,7 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**Overlearning Reveals Sensitive Attributes**](https://openreview.net/pdf?id=SJeNz04tDS) (Song C. et al., 2020) ([code](https://drive.google.com/file/d/1hu0PhN3pWXe6LobxiPFeYBm8L-vQX2zJ/view?usp=sharing))
 
 ## Model extraction
-- [**Stealing machine learning models via prediction apis**](https://www.usenix.org/system/files/conference/usenixsecurity16/sec16_paper_tramer.pdf) (Tramèr et al., 2016) ([code](https://github.com/ftramer/Steal-ML))
+- [**Stealing machine learning models via prediction apis**](https://www.usenix.org/system/files/conference/usenixsecurity16/sec16_paper_tramer.pdf) (Tramèr et al., 2016) ([code](https://github.com/ftramer/Steal-ML)) ([code](https://github.com/ftramer/Steal-ML))
 - [**Stealing hyperparameters in machine learning**](https://ieeexplore.ieee.org/iel7/8418581/8418583/08418595.pdf)(Wang B. et al., 2018)
 - [**Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data**](https://ieeexplore.ieee.org/document/8489592) (Correia-Silva et al., 2018) ([code](https://github.com/jeiks/Stealing_DL_Models))
 - [**Towards reverse-engineering black-box neural networks.**](https://openreview.net/forum?id=BydjJte0-)(Oh et al., 2018) ([code](https://github.com/coallaoh/WhitenBlackBox))
@@ -66,3 +69,11 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**Exploring connections between active learning and model extraction**](https://www.usenix.org/system/files/sec20summer_chandrasekaran_prepub.pdf) (Chandrasekaran et al., 2020)
 - [**High Accuracy and High Fidelity Extraction of Neural Networks**](https://www.usenix.org/conference/usenixsecurity20/presentation/jagielski) (Jagielski et al., 2020)
 - [**Thieves on Sesame Street! Model Extraction of BERT-based APIs**](https://openreview.net/attachment?id=Byl5NREFDr&name=original_pdf) (Krishna et al., 2020) ([code](https://github.com/google-research/language/tree/master/language/bert_extraction))
+- [**Cryptanalytic Extraction of Neural Network Models**](https://arxiv.org/pdf/2003.04884.pdf) (Carlini et al., 2020) 
+- [**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) 
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