Recommended Reading List for the Course of Privacy Computing

Overview

• Books
• Presentation Papers

Books

1. Differential Privacy: From Theory to Practice Ninghui Li, Min Lyu, Dong Su, Weining Yang. Synthesis Lectures on Information Security, Privacy, and Trust, Morgan & Claypool Publishers 2016. book

2. A Pragmatic Introduction to Secure Multi-Party Computation David Evans, Vladimir Kolesnikov, Mike Rosulek. Foundations and Trends in Privacy and Security 2018. book

3. Introduction to Modern Cryptography Jonathan Katz, Yehuda Lindell. CRC press 2020. book

Presentation Papers

1. Federated Learning

1.1 Personalized Federated Learning: Part A

1. DM-PFL: Hitchhiking Generic Federated Learning for Efficient Shift-Robust Personalization Wenhao Zhang, Zimu Zhou, Yansheng Wang, Yongxin Tong. KDD 2023. Paper Link

2. FedALA: Adaptive Local Aggregation for Personalized Federated Learning Jianqing Zhang, Yang Hua, Hao Wang, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan. AAAI 2023. Paper Link

3. PerFedMask: Personalized Federated Learning with Optimized Masking Vectors Mehdi Setayesh, Xiaoxiao Li, Vincent W. S. Wong. ICLR 2023. Paper Link

4. Personalized Federated Learning with Feature Alignment and Classifier Collaboration Jian Xu, Xinyi Tong, Shao-Lun Huang. ICLR 2023. Paper Link

1.2 Federated Graph Neural Network: Part A

1. Automated Graph Neural Network Search Under Federated Learning Framework Chunnan Wang, Bozhou Chen, Geng Li, Hongzhi Wang. TKDE 2023. Paper Link

2. Vertical Federated Graph Neural Network for Recommender System Peihua Mai, Yan Pang. ICML 2023. Paper Link

3. FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks Xinyu Fu, Irwin King. IJCAI 2023. Paper Link

4. Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification Chaochao Chen, Jun Zhou, Longfei Zheng, Huiwen Wu, Lingjuan Lyu, Jia Wu, Bingzhe Wu, Ziqi Liu, Li Wang, Xiaolin Zheng. IJCAI 2022. Paper Link

1.3 Attack/Defense in Federated Learning

1. FLSG: A Novel Defense Strategy Against Inference Attacks in Vertical Federated Learning Kai Fan, Jingtao Hong, Wenjie Li, Xingwen Zhao, Hui Li, Yintang Yang. IEEE Internet Things J. 11(2). Paper Link

2. Privacy-Enhancing and Robust Backdoor Defense for Federated Learning on Heterogeneous Data Zekai Chen, Shengxing Yu, Mingyuan Fan, Ximeng Liu, Robert H. Deng. IEEE Trans. Inf. Forensics Secur. Paper Link

3. Robust and Secure Federated Learning Against Hybrid Attacks: A Generic Architecture Xiaohan Hao, Chao Lin, Wenhan Dong, Xinyi Huang, Hui Xiong. IEEE Trans. Inf. Forensics Secur. Paper Link

4. Data-Agnostic Model Poisoning Against Federated Learning: A Graph Autoencoder Approach Kai Li, Jingjing Zheng, Xin Yuan, Wei Ni, Özgür B. Akan, H. Vincent Poor. IEEE Trans. Inf. Forensics Secur. Paper Link

1.4 Personalized Federated Learning: Part B

1. EchoPFL: Asynchronous Personalized Federated Learning on Mobile Devices with On-Demand Staleness Control Xiaochen Li, Sicong Liu, Zimu Zhou, Bin Guo, Yuan Xu, Zhiwen Yu. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 8(1): 41:1-41:22 (2024). Paper Link

2. Hierarchical Clustering-based Personalized Federated Learning for Robust and Fair Human Activity Recognition Youpeng Li, Xuyu Wang, Lingling An. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 7(1): 20:1-20:38 (2023). Paper Link

3. Personalized Federated Learning With Differential Privacy and Convergence Guarantee Kang Wei, Jun Li, Chuan Ma, Ming Ding, Wen Chen, Jun Wu, Meixia Tao, H. Vincent Poor. IEEE Trans. Inf. Forensics Secur. 18: 4488-4503 (2023). Paper Link

4. Towards Personalized Federated Learning. IEEE Trans. Neural Networks Learn Alysa Ziying Tan, Han Yu, Lizhen Cui, Qiang Yang. IEEE Trans. Neural Networks Learn. Syst. 34(12): 9587-9603 (2023). Paper Link

1.5 Federated Graph Neural Network: Part B

1. FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks Yuhang Yao, Weizhao Jin, Srivatsan Ravi, Carlee Joe-Wong. NeurIPS 2023. Paper Link

2. Bkd-FedGNN: A Benchmark for Classification Backdoor Attacks on Federated Graph Neural Network Fan Liu, Siqi Lai, Yansong Ning, Hao Liu. CoRR abs/2306.10351 (2023). Paper Link

3. No prejudice! Fair Federated Graph Neural Networks for Personalized Recommendation Nimesh Agrawal, Anuj Kumar Sirohi, Jayadeva, Sandeep Kumar. CoRR abs/2312.10080 (2023). Paper Link

4. Federated Graph Neural Networks: Overview, Techniques and Challenges Rui Liu, Han Yu. CoRR abs/2202.07256 (2022). Paper Link

2. Large Language Model

2.1 Security & Privacy issues in LLM: Part A

1. A Survey on Large Language Model (LLM) Security and Privacy: The Good the Bad and the Ugly Yifan Yao, Jinhao Duan, Kaidi Xu, Yuanfang Cai, Eric Sun, Yue Zhang. CoRR abs/2312.02003 (2023). Paper Link

2. Security and Privacy Challenges of Large Language Models: A Survey Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu. CoRR abs/2402.00888 (2024). Paper Link

3. Privacy in Large Language Models: Attacks Defenses and Future Directions Haoran Li, Yulin Chen, Jinglong Luo, Yan Kang, Xiaojin Zhang, Qi Hu, Chunkit Chan, Yangqiu Song. CoRR abs/2310.10383 (2023). Paper Link

4. LLMs Can Understand Encrypted Prompt: Towards Privacy-Computing Friendly Transformer Xuanqi Liu, Zhuotao Liu. CoRR abs/2305.18396 (2023). Paper Link

2.2 Security & Privacy issues in LLM: Part B

1. PrivInfer: Privacy-Preserving Inference for Black-box Large Language Model Meng Tong, Kejiang Chen, Yuang Qi, Jie Zhang, Weiming Zhang, Nenghai Yu. CoRR abs/2310.12214 (2023). Paper Link

2. SecFormer: Towards Fast and Accurate Privacy-Preserving Inference for Large Language Models Jinglong Luo, Yehong Zhang, Jiaqi Zhang, Xin Mu, Hui Wang, Yue Yu, Zenglin Xu. CoRR abs/2401.00793 (2024). Paper Link

3. Beyond Memorization: Violating Privacy Via Inference with Large Language Models Robin Staab, Mark Vero, Mislav Balunovic, Martin T. Vechev. CoRR abs/2310.07298 (2023). Paper Link

4. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, Bo Li. NeurIPS 2023. Paper Link

2.3 Privacy-Preserving Tuning in LLM

1. PrivateLoRA For Efficient Privacy Preserving LLM Yiming Wang, Yu Lin, Xiaodong Zeng, Guannan Zhang. CoRR abs/2311.14030 (2023). Paper Link

2. Privacy-Preserving Prompt Tuning for Large Language Model Services Yansong Li, Zhixing Tan, Yang Liu. CoRR abs/2305.06212 (2023). Paper Link

3. Just Fine-tune Twice: Selective Differential Privacy for Large Language Models Weiyan Shi, Ryan Shea, Si Chen, Chiyuan Zhang, Ruoxi Jia, Zhou Yu. EMNLP 2022. Paper Link

4. EW-Tune: A Framework for Privately Fine-Tuning Large Language Models with Differential Privacy Rouzbeh Behnia, MohammadReza Ebrahimi, Jason Pacheco, Balaji Padmanabhan. ICDM (Workshops) 2022: 560-566. Paper Link

2.4 Secure Code Generation by LLM

1. A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends Zibin Zheng, Kaiwen Ning, Yanlin Wang, Jingwen Zhang, Dewu Zheng, Mingxi Ye, Jiachi Chen. CoRR abs/2311.10372 (2023). Paper Link

2. Enhancing Large Language Models for Secure Code Generation: A Dataset-driven Study on Vulnerability Mitigation Jiexin Wang, Liuwen Cao, Xitong Luo, Zhiping Zhou, Jiayuan Xie, Adam Jatowt, Yi Cai. CoRR abs/2310.16263 (2023). Paper Link

3. Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants Gustavo Sandoval, Hammond Pearce, Teo Nys, Ramesh Karri, Siddharth Garg, Brendan Dolan-Gavitt. USENIX Security Symposium 2023: 2205-2222. Paper Link

4. Large Language Models for Code: Security Hardening and Adversarial Testing Jingxuan He, Martin T. Vechev. CCS 2023: 1865-1879. Paper Link

3. Privacy-Preserving Data Analytics

3.1 Data Federation

1. FedKNN: Secure Federated k-Nearest Neighbor Search Xinyi Zhang, Qichen Wang, Cheng Xu, Yun Peng, and Jianliang Xu. SIGMOD 2024. Paper Link

2. Hu-Fu: Efficient and Secure Spatial Queries over Data Federation Yongxin Tong, Xuchen Pan, Yuxiang Zeng, Yexuan Shi, Chunbo Xue, Zimu Zhou, Xiaofei Zhang, Lei Chen, Yi Xu, Ke Xu, Weifeng Lv. Proc. VLDB Endow. 15(6): 1159-1172 (2022). Paper Link

3. Approximate k-Nearest Neighbor Query over Spatial Data Federation Kaining Zhang, Yongxin Tong, Yexuan Shi, Yuxiang Zeng, Yi Xu, Lei Chen, Zimu Zhou, Ke Xu, Weifeng Lv, Zhiming Zheng. DASFAA (1) 2023: 351-368. Paper Link

4. Fed-LTD: Towards Cross-Platform Ride Hailing via Federated Learning to Dispatch Yansheng Wang, Yongxin Tong, Zimu Zhou, Ziyao Ren, Yi Xu, Guobin Wu, Weifeng Lv. KDD 2022. Paper Link

3.2 Privacy-Preserving Query Processing

1. Longshot: Indexing Growing Databases using MPC and Differential Privacy Yanping Zhang, Johes Bater, Kartik Nayak, Ashwin Machanavajjhala. Proc. VLDB Endow. 16(8): 2005-2018 (2023). Paper Link

2. R2T: Instance-optimal Truncation for Differentially Private Query Evaluation with Foreign Keys Wei Dong, Juanru Fang, Ke Yi, Yuchao Tao, Ashwin Machanavajjhala. SIGMOD 2022. Paper Link

3. DPXPlain: Privately Explaining Aggregate Query Answers Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy. Proc. VLDB Endow. 16(1): 113-126 (2022). Paper Link

4. Efficient and Private Federated Trajectory Matching Yuxiang Wang, Yuxiang Zeng, Yi Xu, Zimu Zhou, Yongxin Tong. CoRR abs/2312.12012 (2023). Paper Link

4. Differential Privacy

4.1 Shuffle-model based Differential Privacy

1. Collecting and analyzing key-value data under shuffled differential privacy Ning Wang, Wei Zheng, Zhigang Wang, Zhiqiang Wei, Yu Gu, Peng Tang, Ge Yu. Frontiers Comput. Sci. 17(2): 172606 (2022). Paper Link

2. Aggregation and Transformation of Vector-Valued Messages in the Shuffle Model of Differential Privacy Mary Scott, Graham Cormode, Carsten Maple. IEEE Trans. Inf. Forensics Secur. 17: 612-627 (2022). Paper Link

3. Shuffled Model of Differential Privacy in Federated Learning Antonious M. Girgis, Deepesh Data, Suhas N. Diggavi, Peter Kairouz, Ananda Theertha Suresh. AISTATS 2021. Paper Link

4. AdaSTopk: Adaptive federated shuffle model based on differential privacy Qiantao Yang, Xuehui Du, Aodi Liu, Na Wang, Wenjuan Wang, Xiangyu Wu. Inf. Sci. 642: 119186 (2023). Paper Link

4.2 Local Differential Privacy

1. PPeFL: Privacy-Preserving Edge Federated Learning With Local Differential Privacy Baocang Wang, Yange Chen, Hang Jiang, Zhen Zhao. IEEE Internet Things J. 10(17): 15488-15500 (2023). Paper Link

2. On the Risks of Collecting Multidimensional Data Under Local Differential Privacy Héber Hwang Arcolezi, Sébastien Gambs, Jean-François Couchot, Catuscia Palamidessi. Proc. VLDB Endow. 16(5): 1126-1139 (2023). Paper Link

3. Multi-Dimensional Data Publishing With Local Differential Privacy Gaoyuan Liu, Peng Tang, Chengyu Hu, Chongshi Jin, Shanqing Guo. EDBT 2023. Paper Link

4. Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework Yansheng Wang, Yongxin Tong, Dingyuan Shi. AAAI 2020. Paper Link

4.3 Differential Privacy Based Federated Learning

1. Differential Privacy in HyperNetworks for Personalized Federated Learning Vaisnavi Nemala, Phung Lai, NhatHai Phan. CIKM 2023: 4224-4228. Paper Link

2. Personalized Differentially Private Federated Learning without Exposing Privacy Budgets Junxu Liu, Jian Lou, Li Xiong, Xiaofeng Meng. CIKM 2023: 4140-4144. Paper Link

3. Cross-silo Federated Learning with Record-level Personalized Differential Privacy Junxu Liu, Jian Lou, Li Xiong, Jinfei Liu, Xiaofeng Meng. CoRR abs/2401.16251 (2024). Paper Link

4. Personalized Federated Learning With Differential Privacy and Convergence Guarantee Kang Wei, Jun Li, Chuan Ma, Ming Ding, Wen Chen, Jun Wu, Meixia Tao, H. Vincent Poor. IEEE Trans. Inf. Forensics Secur. 18: 4488-4503 (2023). Paper Link

5. Secure Multi-party Computation & Encryption

5.1 Private Set Intersection: Part A

1. Differentially Private Set Intersection for Asymmetrical ID Alignment Zitao Li, Tianhao Wang, Ninghui Li. Proc. VLDB Endow. 16(6): 1277-1290 (2023) Paper Link

2. Federated K-Private Set Intersection Ahmed Roushdy Elkordy, Yahya H. Ezzeldin, Salman Avestimehr. CIKM 2022. Paper Link

3. Laconic Private Set-Intersection From Pairings Diego F. Aranha, Chuanwei Lin, Claudio Orlandi, Mark Simkin. CCS 2022. Paper Link

4. Towards Practical Data Alignment in Production Federated Learning Yexuan Shi, Wei Yu, Yuanyuan Zhang, Chunbo Xue, Yuxiang Zeng, Zimu Zhou, Manxue Guo, Lun Xin, Wenjing Nie. Front. Comput. Sci., 2024, 1(1): 1–18. Paper Link

5.2 Encryption Based Federated Learning

1. A Multi-Modal Vertical Federated Learning Framework Based on Homomorphic Encryption Maoguo Gong, Yuanqiao Zhang, Yuan Gao, A. Kai Qin, Yue Wu, Shanfeng Wang, Yihong Zhang. IEEE Trans. Inf. Forensics Secur. 19: 1826-1839 (2024) Paper Link

2. Privacy-Preserving Federated Learning via Functional Encryption Revisited Yansong Chang, Kai Zhang, Junqing Gong, Haifeng Qian. IEEE Trans. Inf. Forensics Secur. 18: 1855-1869 (2023) Paper Link

3. CryptoFE: Practical and Privacy-Preserving Federated Learning via Functional Encryption Xinyuan Qian, Hongwei Li, Meng Hao, Shuai Yuan, Xilin Zhang, Song Guo. GLOBECOM 2022. Paper Link

4. BatchCrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning Chengliang Zhang, Suyi Li, Junzhe Xia, Wei Wang, Feng Yan, Yang Liu. USENIX Annual Technical Conference 2020. Paper Link

5.3 Private Set Intersection: Part B

1. Faster Secure Comparisons with Offline Phase for Efficient Private Set Intersection Florian Kerschbaum, Erik-Oliver Blass, Rasoul Akhavan Mahdavi. NDSS 2023. Paper Link

2. Efficient Private Multiset ID protocols Cong Zhang, Weiran Liu, Bolin Ding, Dongdai Lin. ICICS 2023: 351-369. Paper Link

3. Split, count, and share: a differentially private set intersection cardinality estimation protocol Michael Purcell, Yang Li, Kee Siong Ng. UAI 2023: 1684-1694. Paper Link

4. Distance-Aware Private Set Intersection Anrin Chakraborti, Giulia Fanti, Michael K. Reiter. USENIX Security Symposium 2023: 319-336. Paper Link