Merge pull request #7 from suhacker1/master

Add more papers

README.md

@@ -68,6 +68,7 @@ This repository contains a curated list of papers related to privacy attacks aga
 - [**Bootstrap Aggregation for Point-based Generalized Membership Inference Attacks**](https://arxiv.org/abs/2011.08738) (Felps et al., 2020)
 - [**Differentially Private Learning Does Not Bound Membership Inference**](https://arxiv.org/abs/2010.12112) (Humphries et al., 2020)
 - [**Quantifying Membership Privacy via Information Leakage**](https://arxiv.org/abs/2010.05965) (Saeidian et al., 2020)
+- [**Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning**](https://arxiv.org/abs/1906.00389) (Yaghini et al., 2020)
 
 ## Reconstruction
 Reconstruction attacks cover also attacks known as *model inversion* and *attribute inference*.
@@ -112,6 +113,7 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**Property inference attacks on fully connected neural networks using permutation invariant representations**](https://dl.acm.org/doi/pdf/10.1145/3243734.3243834) (Ganju et al., 2018)
 - [**Exploiting unintended feature leakage in collaborative learning**](https://ieeexplore.ieee.org/iel7/8826229/8835208/08835269.pdf) (Melis et al., 2019) ([code](https://github.com/csong27/property-inference-collaborative-ml))
 - [**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))
+- [**Subject Property Inference Attack in Collaborative Learning**](https://ieeexplore.ieee.org/document/9204357) (Xu and Li, 2020)
 
 ## 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)) 
@@ -145,6 +147,7 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles**](https://arxiv.org/abs/2009.09560) (Yuan et al., 2020)
 - [**Black-Box Ripper: Copying black-box models using generative evolutionary algorithms**](https://arxiv.org/abs/2010.11158) (Barbalau et al., 2020) ([code](https://github.com/antoniobarbalau/black-box-ripper))
 - [**Model Extraction Attacks on Graph Neural Networks: Taxonomy and Realization**](https://arxiv.org/abs/2010.12751) (Wu et al., 2020) 
+- [**Extracting Training Data from Large Language Models**](https://arxiv.org/abs/2012.07805) (Carlini et al., 2020)
 
 # Other
 - [**Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy**](https://arxiv.org/abs/2009.03561) (Naseri et al., 2020)
@@ -153,3 +156,5 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**Information Leakage in Embedding Models**](https://arxiv.org/abs/2004.00053) (Song and Raghunathan, 2020)
 - [**Hide-and-Seek Privacy Challenge**](https://arxiv.org/abs/2007.12087) (Jordan et al., 2020)
 - [**Synthetic Data -- A Privacy Mirage**](https://arxiv.org/abs/2011.07018) (Stadler et al., 2020)
+- [**Robust Membership Encoding: Inference Attacks and CopyrightProtection for Deep Learning**](https://arxiv.org/pdf/1909.12982.pdf) (Song and Shokri, 2020)
+- [**Quantifying Privacy Leakage in Graph Embedding**](https://arxiv.org/abs/2010.00906) (Duddu et al., 2020)