Added two more papers

README.md

@@ -16,10 +16,10 @@ This repository contains a curated list of papers related to privacy attacks aga
 - [**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)
 - [**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)) 
-- [**Membership inference attack against differentially private deep learning model**](http://www.tdp.cat/issues16/tdp.a289a17.pdf) (Rahman ett al., 2018)
 - [**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))
 - [**White-box vs Black-box: Bayes Optimal Strategies for Membership Inference**](http://proceedings.mlr.press/v97/sablayrolles19a.html) (Sablayrolles etal., 2019)
@@ -28,7 +28,9 @@ This repository contains a curated list of papers related to privacy attacks aga
 - [**Monte carlo and reconstruction membership inference attacks against generative models**](https://content.sciendo.com/view/journals/popets/2019/4/article-p232.xml) (Hilprecht et al., 2019)
 - [**MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples**](https://arxiv.org/abs/1909.10594) (Jia et al., 2019) ([code](https://github.com/jjy1994/MemGuard))
 - [**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)
 
 ## Reconstruction
 Reconstruction attacks cover also attacks known as *model inversion* and *attribute inference*.
@@ -46,7 +48,7 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**iDLG: Improved Deep Leakage from Gradients**](https://arxiv.org/pdf/2001.02610) (Zhao et al., 2020) ([code](https://github.com/PatrickZH/Improved-Deep-Leakage-from-Gradients))
 - [**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)) ([link](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)
+- [**Inverting Gradients - How easy is it to break privacy in federated learning?**](https://arxiv.org/abs/2003.14053) (Geiping et al., 2020)
 
 
 ## Property inference