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@@ -22,6 +22,7 @@ This repository contains a curated list of papers related to privacy attacks aga
 - [**Membership Inference Attacks on Machine Learning: A Survey**](https://arxiv.org/abs/2103.07853) (Hu et al., 2021)
 - [**Survey: Leakage and Privacy at Inference Time**](https://arxiv.org/abs/2107.01614) (Jegorova et al., 2021)
 - [**A Review of Confidentiality Threats Against Embedded Neural Network Models**](https://arxiv.org/abs/2105.01401) (Joud et al., 2021)
+- [**Federated Learning Attacks Revisited: A Critical Discussion of Gaps,Assumptions, and Evaluation Setups**](https://arxiv.org/abs/2111.03363) (Wainakh et al., 2021)
 
 # Privacy Testing Tools
 - [**PrivacyRaven**](https://github.com/trailofbits/PrivacyRaven) (Trail of Bits)
@@ -167,6 +168,7 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
 - [**Property Inference From Poisoning**](https://arxiv.org/abs/2101.11073) (Chase et al., 2021)
 - [**Property Inference Attacks on Convolutional Neural Networks: Influence and Implications of Target Model's Complexity**](https://arxiv.org/abs/2104.13061) (Parisot et al., 2021)
 - [**Honest-but-Curious Nets: Sensitive Attributes of Private Inputs can be Secretly Coded into the Entropy of Classifiers' Outputs**](https://arxiv.org/abs/2105.12049) (Malekzadeh et al. 2021) ([code](https://github.com/mmalekzadeh/honest-but-curious-nets))
+- [**Property Inference Attacks Against GANs**](https://arxiv.org/abs/2111.07608) (Zhou et al., 2021) ([code](https://github.com/Zhou-Junhao/PIA_GAN))
 
 ## 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))