Added CSI NN paper and TOC

README.md

@@ -1,6 +1,15 @@
 # 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.
 
+# Contents
+- [Surveys and Overviews](#surveys-and-overviews)
+- [Papers and Code](#papers-and-code)
+  * [Membership inference](#membership-inference)
+  * [Reconstruction](#reconstruction)
+  * [Property inference](#property-inference)
+  * [Model extraction](#model-extraction)
+
+
 # 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)
@@ -84,3 +93,4 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**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) 
+- [**CSI NN: Reverse Engineering of Neural Network Architectures Through Electromagnetic Side Channel**](https://www.usenix.org/system/files/sec19-batina.pdf) (Batina et al., 2019)