Add new paper for awesome-pets/aml (secretflow#553)

* Update attack_defense.md

* Update awesome-pets.md

docs/awesome-pets/awesome-pets.md

@@ -37,7 +37,7 @@ Protecting training/inference data
 
 Attacks on machine learning system
 
-1. [General attacks and defense](papers/applications/aml/attack_defense.md) (Contributors: [@zhangxingmeng](https://www.github.com/zhangxingmeng))
+1. [General attacks and defense](papers/applications/aml/attack_defense.md) (Contributors: [@zhangxingmeng](https://www.github.com/zhangxingmeng) [@JDywz](https://github.com/JdYwz))
 
 Multimedia Privacy and Security
 

docs/awesome-pets/papers/applications/aml/attack_defense.md

@@ -1,47 +1,66 @@
 ## Attack
+
 [An Overview of Federated Deep Learning Privacy Attacks and Defensive Strategies. 2020-04-01](https://arxiv.org/pdf/2004.04676.pdf)
+
 ### Backdoor Attack
+
 - [How To Backdoor Federated Learning](https://arxiv.org/pdf/1807.00459.pdf)
 
 - [Can You Really Backdoor Federated Learning?](https://arxiv.org/abs/1911.07963)
+
 - [Attack of the Tails: Yes, You Really Can Backdoor Federated Learning](https://papers.nips.cc/paper/2020/file/b8ffa41d4e492f0fad2f13e29e1762eb-Paper.pdf)
 
 - [DBA: Distributed Backdoor Attacks against Federated Learning](https://openreview.net/pdf?id=rkgyS0VFvr)
 
 - [CRFL: Certifiably Robust Federated Learning against Backdoor Attacks. ICML 2021.](https://arxiv.org/pdf/2106.08283.pdf)
 
-
 - [NeurIPS 2020 Submission: Backdoor Attacks on Federated Meta-Learning](https://arxiv.org/pdf/2006.07026.pdf)
 
 ### Gradients Attack
+
 - [Deep Leakage from Gradients](https://arxiv.org/pdf/1906.08935.pdf)
+
 - [Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix](https://arxiv.org/pdf/2106.06089.pdf)
--[iDLG: Improved Deep Leakage from Gradients](https://arxiv.org/pdf/2001.02610.pdf)
+
+- [iDLG: Improved Deep Leakage from Gradients](https://arxiv.org/pdf/2001.02610.pdf)
 
 - [Inverting Gradients - How easy is it to break Privacy in Federated Learning?](https://arxiv.org/pdf/2003.14053.pdf)
 
 - [CAFE: Catastrophic Data Leakage in Vertical Federated Learning](https://papers.neurips.cc/paper/2021/file/08040837089cdf46631a10aca5258e16-Paper.pdf)
 
 - [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning.](https://arxiv.org/pdf/1702.07464.pdf)
+
 - [A Framework for Evaluating Gradient Leakage Attacks in Federated Learning](https://arxiv.org/pdf/2004.10397.pdf)
-- [Inverting Gradients - How easy is it to break privacy in federated learning? 2020-03-31](https://arxiv.org/pdf/2003.14053.pdf)
+
+- [Gradient Inversion with Generative Image Prior. NeurIPS 2021.](https://arxiv.org/pdf/2110.14962.pdf)
+
+- [Evaluating Gradient Inversion Attacks and Defenses in Federated Learning. NeurIPS 2021.](https://arxiv.org/pdf/2112.00059.pdf)
 
 ### Model Poison Attack
+
 - [Analyzing Federated Learning through an Adversarial Lens](https://arxiv.org/abs/1811.12470)
 - [(*) Local Model Poisoning Attacks to Byzantine-Robust Federated Learning. 2019-11-26](https://arxiv.org/pdf/1911.11815.pdf)
 - [Data Poisoning Attacks on Federated Machine Learning. 2020-04-19](https://arxiv.org/pdf/2004.10020.pdf)
 
 ### Free Rider Attack
+
 - [NeurIPS 2020 submission: Free-rider Attacks on Model Aggregation in Federated Learning](https://arxiv.org/pdf/2006.11901.pdf)
 - [Free-riders in Federated Learning: Attacks and Defenses. 2019-11-28](https://arxiv.org/pdf/1911.12560.pdf)
 
 ### Membership Inference Attack
 
+- [Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning. S&P 2019.](https://arxiv.org/pdf/1812.00910.pdf)
+
 ### Feature Inference Attack
 
+### Label Inference Attack
+
+- [Label Inference Attacks Against Vertical Federated Learning. USENIX Security 2022.](https://www.usenix.org/system/files/sec22-fu-chong.pdf)
+
 ## Defense
 
 ### With DP
+
 - [Differentially Private Federated Learning: A Client Level Perspective. NIPS 2017 Workshop](https://arxiv.org/pdf/1712.07557.pdf)
 
 - [Federated Learning with Bayesian Differential Privacy.](https://arxiv.org/pdf/1911.10071.pdf)
@@ -56,8 +75,8 @@
 
 ### With TEE
 
-
 ### Algorithm
+
 - [RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets, AAAI 2019](https://arxiv.org/abs/1811.03761)
 
 - [Towards Realistic Byzantine-Robust Federated Learning. 2020-04-10](https://arxiv.org/pdf/2004.04986.pdf)
@@ -67,6 +86,7 @@
 - [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://arxiv.org/pdf/1803.01498.pdf)
 
 ## Fairness
+
 - [Fair Resource Allocation in Federated Learning. ICLR 2020.](https://arxiv.org/pdf/1905.10497.pdf)
 
 - [Hierarchically Fair Federated Learning](https://arxiv.org/pdf/2004.10386.pdf)