Awesome Privacy

Key papers

• PRADA: Protecting Against DNN Model Stealing Attacks (2018)
  Mika Juuti, Sebastian Szyller, Samuel Marchal, et al.
  18.53
• Survey On Federated Learning Threats: Concepts, Taxonomy On Attacks And Defences, Experimental Study And Challenges (2022)
  Nuria Rodríguez-Barroso, Daniel Jiménez López, M. Victoria Luzón, et al.
  18.11
• Data Poisoning Attacks On Federated Machine Learning (2020)
  Gan Sun, Yang Cong, Jiahua Dong, et al.
  17.63
• MPAF: Model Poisoning Attacks To Federated Learning Based On Fake Clients (2022)
  Xiaoyu Cao, Neil Zhenqiang Gong
  16.79
• Privacy Risks Of Securing Machine Learning Models Against Adversarial Examples (2019)
  Liwei Song, Reza Shokri, Prateek Mittal
  16.71
• Deep K-nn Defense Against Clean-label Data Poisoning Attacks (2019)
  Neehar Peri, Neal Gupta, W. Ronny Huang, et al.
  16.44
• Provable Defense Against Privacy Leakage In Federated Learning From Representation Perspective (2020)
  Jingwei Sun, Ang Li, Binghui Wang, et al.
  16.21
• Enhanced Membership Inference Attacks Against Machine Learning Models (2021)
  Jiayuan Ye, Aadyaa Maddi, Sasi Kumar Murakonda, et al.
  15.78
• Fedrecover: Recovering From Poisoning Attacks In Federated Learning Using Historical Information (2022)
  Xiaoyu Cao, Jinyuan Jia, Zaixi Zhang, et al.
  15.10
• Eluding Secure Aggregation In Federated Learning Via Model Inconsistency (2021)
  Dario Pasquini, Danilo Francati, Giuseppe Ateniese
  14.58
• Adversary-resilient Distributed And Decentralized Statistical Inference And Machine Learning: An Overview Of Recent Advances Under The Byzantine Threat Model (2019)
  Zhixiong Yang, Arpita Gang, Waheed U. Bajwa
  14.35
• Shielding Collaborative Learning: Mitigating Poisoning Attacks Through Client-side Detection (2019)
  Lingchen Zhao, Shengshan Hu, Qian Wang, et al.
  14.19
• Backdoor Attacks On Pre-trained Models By Layerwise Weight Poisoning (2021)
  Linyang Li, Demin Song, Xiaonan Li, et al.
  13.74
• Byzantine-resilient Decentralized Stochastic Gradient Descent (2020)
  Shangwei Guo, Tianwei Zhang, Han Yu, et al.
  13.70
• Byzantine-robust Federated Learning Through Collaborative Malicious Gradient Filtering (2021)
  Jian Xu, Shao-Lun Huang, Linqi Song, et al.
  13.70
• Poisoning Web-scale Training Datasets Is Practical (2023)
  Nicholas Carlini, Matthew Jagielski, Christopher A. Choquette-Choo, et al.
  13.50
• Practical Blind Membership Inference Attack Via Differential Comparisons (2021)
  Bo Hui, Yuchen Yang, Haolin Yuan, et al.
  13.44
• Membership Inference Attacks And Defenses In Classification Models (2020)
  Jiacheng Li, Ninghui Li, Bruno Ribeiro
  13.23
• Black-box Attacks On Sequential Recommenders Via Data-free Model Extraction (2021)
  Zhenrui Yue, Zhankui He, Huimin Zeng, et al.
  13.17
• Quantifying Privacy Risks Of Masked Language Models Using Membership Inference Attacks (2022)
  Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, et al.
  13.11
• Multi-metrics Adaptively Identifies Backdoors In Federated Learning (2023)
  Siquan Huang, Yijiang Li, Chong Chen, et al.
  12.47
• Desmp: Differential Privacy-exploited Stealthy Model Poisoning Attacks In Federated Learning (2021)
  Md Tamjid Hossain, Shafkat Islam, Shahriar Badsha, et al.
  11.76
• Less Is More: A Privacy-respecting Android Malware Classifier Using Federated Learning (2020)
  Rafa Gálvez, Veelasha Moonsamy, Claudia Diaz
  11.39
• Shielding Federated Learning: Robust Aggregation With Adaptive Client Selection (2022)
  Wei Wan, Shengshan Hu, Jianrong Lu, et al.
  11.29
• Time-aware Gradient Attack On Dynamic Network Link Prediction (2019)
  Jinyin Chen, Jian Zhang, Zhi Chen, et al.
  11.08
• Freqfed: A Frequency Analysis-based Approach For Mitigating Poisoning Attacks In Federated Learning (2023)
  Hossein Fereidooni, Alessandro Pegoraro, Phillip Rieger, et al.
  10.85
• Splitguard: Detecting And Mitigating Training-hijacking Attacks In Split Learning (2021)
  Ege Erdogan, Alptekin Kupcu, A. Ercument Cicek
  10.48
• Baybfed: Bayesian Backdoor Defense For Federated Learning (2023)
  Kavita Kumari, Phillip Rieger, Hossein Fereidooni, et al.
  10.48
• Feddefender: Client-side Attack-tolerant Federated Learning (2023)
  Sungwon Park, Sungwon Han, Fangzhao Wu, et al.
  10.48
• Formalizing And Estimating Distribution Inference Risks (2021)
  Anshuman Suri, David Evans
  10.48
• Federated Robustness Propagation: Sharing Robustness In Heterogeneous Federated Learning (2021)
  Junyuan Hong, Haotao Wang, Zhangyang Wang, et al.
  10.07
• On The (in)security Of Peer-to-peer Decentralized Machine Learning (2022)
  Dario Pasquini, Mathilde Raynal, Carmela Troncoso
  10.07
• Hashvfl: Defending Against Data Reconstruction Attacks In Vertical Federated Learning (2022)
  Pengyu Qiu, Xuhong Zhang, Shouling Ji, et al.
  10.07
• Tapfed: Threshold Secure Aggregation For Privacy-preserving Federated Learning (2025)
  Runhua Xu, Bo Li, Chao Li, et al.
  10.05
• Identifying Malicious Web Domains Using Machine Learning Techniques With Online Credibility And Performance Data (2019)
  Zhongyi Hu, Raymond Chiong, Ilung Pranata, et al.
  9.92
• Privacy Inference-empowered Stealthy Backdoor Attack On Federated Learning Under Non-iid Scenarios (2023)
  Haochen Mei, Gaolei Li, Jun Wu, et al.
  9.92
• Semi-targeted Model Poisoning Attack On Federated Learning Via Backward Error Analysis (2022)
  Yuwei Sun, Hideya Ochiai, Jun Sakuma
  9.92
• Overconfidence Is A Dangerous Thing: Mitigating Membership Inference Attacks By Enforcing Less Confident Prediction (2023)
  Zitao Chen, Karthik Pattabiraman
  9.76
• Backdoor Attacks In Federated Learning By Rare Embeddings And Gradient Ensembling (2022)
  Kiyoon Yoo, Nojun Kwak
  9.76
• Fusion: Efficient And Secure Inference Resilient To Malicious Servers (2022)
  Caiqin Dong, Jian Weng, Jia-Nan Liu, et al.
  9.59
• Zprobe: Zero Peek Robustness Checks For Federated Learning (2022)
  Zahra Ghodsi, Mojan Javaheripi, Nojan Sheybani, et al.
  9.59
• Feddrl: A Trustworthy Federated Learning Model Fusion Method Based On Staged Reinforcement Learning (2023)
  Leiming Chen, Weishan Zhang, Cihao Dong, et al.
  9.41
• Federated And Transfer Learning: A Survey On Adversaries And Defense Mechanisms (2022)
  Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif
  9.23
• With Great Dispersion Comes Greater Resilience: Efficient Poisoning Attacks And Defenses For Linear Regression Models (2020)
  Jialin Wen, Benjamin Zi Hao Zhao, Minhui Xue, et al.
  9.23
• Denial-of-service Or Fine-grained Control: Towards Flexible Model Poisoning Attacks On Federated Learning (2023)
  Hangtao Zhang, Zeming Yao, Leo Yu Zhang, et al.
  9.23
• Defending Against Weight-poisoning Backdoor Attacks For Parameter-efficient Fine-tuning (2024)
  Shuai Zhao, Leilei Gan, Luu Anh Tuan, et al.
  9.23
• QUEEN: Query Unlearning Against Model Extraction (2024)
  Huajie Chen, Tianqing Zhu, Lefeng Zhang, et al.
  9.03
• Evaluating Membership Inference Through Adversarial Robustness (2022)
  Zhaoxi Zhang, Leo Yu Zhang, Xufei Zheng, et al.
  9.03
• ARFED: Attack-resistant Federated Averaging Based On Outlier Elimination (2021)
  Ece Isik-Polat, Gorkem Polat, Altan Kocyigit
  8.82
• Leverage Variational Graph Representation For Model Poisoning On Federated Learning (2024)
  Kai Li, Xin Yuan, Jingjing Zheng, et al.
  8.82
• MEGEX: Data-free Model Extraction Attack Against Gradient-based Explainable AI (2021)
  Takayuki Miura, Satoshi Hasegawa, Toshiki Shibahara
  8.82
• Dropout Is NOT All You Need To Prevent Gradient Leakage (2022)
  Daniel Scheliga, Patrick Mäder, Marco Seeland
  8.82
• Influence Based Defense Against Data Poisoning Attacks In Online Learning (2021)
  Sanjay Seetharaman, Shubham Malaviya, Rosni Kv, et al.
  8.82
• Covert Model Poisoning Against Federated Learning: Algorithm Design And Optimization (2021)
  Kang Wei, Jun Li, Ming Ding, et al.
  8.82
• User Inference Attacks On Large Language Models (2023)
  Nikhil Kandpal, Krishna Pillutla, Alina Oprea, et al.
  8.60
• Practical And General Backdoor Attacks Against Vertical Federated Learning (2023)
  Yuexin Xuan, Xiaojun Chen, Zhendong Zhao, et al.
  8.60
• Membership Inference Attacks Against In-context Learning (2024)
  Rui Wen, Zheng Li, Michael Backes, et al.
  8.35
• Mitigating Backdoor Attack By Injecting Proactive Defensive Backdoor (2024)
  Shaokui Wei, Hongyuan Zha, Baoyuan Wu
  8.14
• Blockchain-empowered Cyber-secure Federated Learning For Trustworthy Edge Computing (2024)
  Ervin Moore, Ahmed Imteaj, Md Zarif Hossain, et al.
  8.09
• Fedbayes: A Zero-trust Federated Learning Aggregation To Defend Against Adversarial Attacks (2023)
  Marc Vucovich, Devin Quinn, Kevin Choi, et al.
  8.09