Add new overview paper; Fix 1 reference

[**Test**](Link)(Ref)

README.md

@@ -4,6 +4,7 @@ This repository contains a curated list of papers related to privacy attacks aga
 # 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
 
@@ -11,7 +12,7 @@ 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)
+- [**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))