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-# Awesome atacks on machine learning privacy   [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
-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.
+# Awesome Attacks on Machine Learning Privacy   [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)
+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.
 
 # Surveys and Overviews
 - [**A Survey of Privacy Attacks in Machine Learning**](https://arxiv.org/abs/2007.07646) (Rigaki and Garcia, 2020)
 - [**An Overview of Privacy in Machine Learning**](https://arxiv.org/pdf/2005.08679) (De Cristofaro, 2020)
+- [**Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks**](https://arxiv.org/abs/2006.11601) (Fan et al., 2020)
  
 # Papers and Code
 
 ## 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))
-- [**Membership inference attack against differentially private deep learning model**](http://www.tdp.cat/issues16/tdp.a289a17.pdf) (Rahman ett 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))
+- [**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))
 - [**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)) 
@@ -25,7 +26,12 @@ This repository contains a curated list of papers related to privacy attacks aga
 - [**Gan-leaks: A taxonomy of membership inference attacks against gans**](https://arxiv.org/pdf/1909.03935.pdf) (Chen,et al., 2019))
 - [**Auditing Data Provenance in Text-Generation Models**](https://dl.acm.org/doi/pdf/10.1145/3292500.3330885) (Song and Shmatikov, 2019)
 - [**Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System?**](https://www.mitpressjournals.org/doi/full/10.1162/tacl_a_00299) (Hisamoto et al., 2020) 
-- [**Revisiting Membership InferenceUnder Realistic Assumptions**](https://arxiv.org/pdf/2005.10881.pdf) (Jayaraman et al., 2020)
+- [**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*.
@@ -44,7 +50,15 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**Privacy Risks of General-Purpose Language Models**](https://www.researchgate.net/profile/Xudong_Pan3/publication/340965355_Privacy_Risks_of_General-Purpose_Language_Models/links/5ea7ca55a6fdccd7945b6a7d/Privacy-Risks-of-General-Purpose-Language-Models.pdf) (Pan et al., 2020)
 - [**The secret revealer: generative model-inversion attacks against deep neural networks**](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_The_Secret_Revealer_Generative_Model-Inversion_Attacks_Against_Deep_Neural_Networks_CVPR_2020_paper.pdf)) (Zhang et al., 2020)
 - [**Inverting Gradients - How easy is it to break privacy in federated learning?**](https://arxiv.org/abs/2003.14053) (Geiping et al., 2020)
-
+- [**GAMIN: An Adversarial Approach to Black-Box Model Inversion**](https://arxiv.org/abs/1909.11835) (Aivodji et al., 2019)
+- [**Adversarial Privacy Preservation under Attribute Inference Attack**](https://arxiv.org/abs/1906.07902) (Zhao et al., 2019) 
+- [**Reconstruction of training samples from loss functions**](https://arxiv.org/pdf/1805.07337.pdf) (Sannai, 2018)
+- [**A Framework for Evaluating Gradient Leakage Attacks in Federated Learning**](https://arxiv.org/pdf/2004.10397.pdf) (Wei et al., 2020) 
+- [**Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning**](https://arxiv.org/pdf/1702.07464.pdf) (Hitaj et al., 2017)
+- [**Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning**](https://arxiv.org/pdf/1812.00535.pdf) (Wang et al., 2018) 
+- [**Exploring Image Reconstruction Attack in Deep Learning Computation Offloading**](https://dl.acm.org/doi/pdf/10.1145/3325413.3329791) (Oh and Lee, 2019) 
+- [**I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators**](https://arxiv.org/pdf/1803.05847.pdf) (Wei et al., 2019)
+- [**Illuminating the Dark or how to recover what should not be seen in FE-based classifiers**](https://eprint.iacr.org/2018/1001) (Carpov 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)
@@ -53,8 +67,8 @@ 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 hyperparameters in machine learning**](https://ieeexplore.ieee.org/iel7/8418581/8418583/08418595.pdf)(Wang B. et al., 2018)
+- [**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 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))
 - [**Knockoff nets: Stealing functionality of black-box models**](http://openaccess.thecvf.com/content_CVPR_2019/papers/Orekondy_Knockoff_Nets_Stealing_Functionality_of_Black-Box_Models_CVPR_2019_paper.pdf) (Orekondy et al., 2019) ([code](https://github.com/tribhuvanesh/knockoffnets))
@@ -63,3 +77,11 @@ 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) 
+- [**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)