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@@ -70,6 +70,13 @@ This repository contains a curated list of papers related to privacy attacks aga
 - [**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)
+- [**Use the Spear as a Shield: A Novel Adversarial Example based Privacy-Preserving Technique against Membership Inference Attacks**](https://arxiv.org/abs/2011.13696) (Xue et al., 2020)
+- [**Towards Realistic Membership Inferences: The Case of Survey Data**](https://dl.acm.org/doi/abs/10.1145/3427228.3427282?casa_token=eHK7DPiTIigAAAAA:sinfqtYoQA8GddIiwn28DYNEG1NsvW42wvUnRLkpBGQKhrI_mawTRV8MOmLGotqaTspYS-eOIp56UQ)
+- [**Unexpected Information Leakage of Differential Privacy Due to Linear Property of Queries**](https://arxiv.org/abs/2010.08958) (Huang et al., 2020)
+- [**TransMIA: Membership Inference Attacks Using Transfer Shadow Training**](https://arxiv.org/abs/2011.14661) (Hidano et al., 2020)
+- [**An Extension of Fano's Inequality for Characterizing Model Susceptibility to Membership Inference Attacks**](https://arxiv.org/abs/2009.08097) (Jha et al., 2020)
+- [**Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning**](https://arxiv.org/abs/2101.04535) (Nasr et al., 2021)
+
 
 ## Reconstruction
 Reconstruction attacks cover also attacks known as *model inversion* and *attribute inference*.
@@ -108,6 +115,13 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**SAPAG: A Self-Adaptive Privacy Attack From Gradients**](https://arxiv.org/abs/2009.06228) (Wang et al., 2020)
 - [**Theory-Oriented Deep Leakage from Gradients via Linear Equation Solver**](https://arxiv.org/abs/2010.13356) (Pan et al., 2020)
 - [**Improved Techniques for Model Inversion Attacks**](https://arxiv.org/abs/2010.04092) (Chen et al., 2020)
+- [**KART: Privacy Leakage Framework of Language Models Pre-trained with Clinical Records**](https://arxiv.org/abs/2101.00036) (Nakamura et al., 2020)
+- [**Black-box Model Inversion Attribute Inference Attacks on Classification Models**](https://arxiv.org/abs/2012.03404) (Mehnaz et al., 2020)
+- [**Deep Face Recognizer Privacy Attack: Model Inversion Initialization by a Deep Generative Adversarial Data Space Discriminator**](https://ieeexplore.ieee.org/abstract/document/9306253?casa_token=H78uIRJ2smYAAAAA:iQiA_5d2a2mbH4oBF9EZwSjakAz3Muq3ZOkNDBkK_fLq19PEMGEvpipyli7d9SGKESglqIb9Ug) (Khosravy et al., 2020) 
+- [**MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery**](https://arxiv.org/abs/2010.11463) (Li et al., 2020)
+- [**Evaluation of Inference Attack Models for Deep Learning on Medical Data**](https://arxiv.org/abs/2011.00177) (Wu et al., 2020)
+- [**FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries**](https://arxiv.org/abs/2010.14023) (Liew and Takahashi, 2020)
+
 
 ## 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)
@@ -116,6 +130,7 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**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)) 
 - [**Stealing hyperparameters in machine learning**](https://ieeexplore.ieee.org/iel7/8418581/8418583/08418595.pdf) (Wang B. et al., 2018)
@@ -149,8 +164,12 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**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)
+- [**Model Extraction Attacks and Defenses on Cloud-Based Machine Learning Models**](https://ieeexplore.ieee.org/abstract/document/9311938?casa_token=f_8Lg24vAQkAAAAA:A7P5ym7bTLFIJZtL2yGorscyQC2R1WGJUKzcO-pn8wADHus0w8NArN-nv0JFcKYhwwQFeCaptQ) (Gong et al., 2020)
+- [**Leveraging Extracted Model Adversaries for Improved Black Box Attacks**](https://arxiv.org/abs/2010.16336) (Nizar and Kobren, 2020)
+- [**Differentially Private Machine Learning Model against Model Extraction Attack**](https://ieeexplore.ieee.org/abstract/document/9291542) (Cheng et al., 2020)
 
 # Other
+- [**Amnesiac Machine Learning**](https://arxiv.org/abs/2010.10981) (Graves et al., 2020)
 - [**Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy**](https://arxiv.org/abs/2009.03561) (Naseri et al., 2020)
 - [**Analyzing Information Leakage of Updates to Natural Language Models**](https://arxiv.org/abs/1912.07942) (Brockschmidt et al., 2020)
 - [**Estimating g-Leakage via Machine Learning**](https://arxiv.org/abs/2005.04399) (Romanelli et al., 2020)