Add side-channel papers

README.md

@@ -57,6 +57,7 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**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)
 
 ## 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)
@@ -81,5 +82,5 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**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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+- [**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)