User:99rebound/Local differential privacy/Bibliography

Bibliography

This is where you will compile the bibliography for your Wikipedia assignment. Please refer to the following resources for help:

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• Evaluating articles and sources

1. Rodríguez-Barroso, Nuria et al. 2020. “Federated Learning and Differential Privacy: Software Tools Analysis, the Sherpa.ai FL Framework and Methodological Guidelines for Preserving Data Privacy.” Information Fusion 64:270–92.

2. Ren, Hao et al. 2016. “Privacy-Enhanced and Multifunctional Health Data Aggregation under Differential Privacy Guarantees.” Sensors 16(9):1463.

3. Wang, Teng, Xuefeng Zhang, Jingyu Feng, and Xinyu Yang. 2020. “A Comprehensive Survey on Local Differential Privacy toward Data Statistics and Analysis.” Sensors 20(24):7030.

4. Aljably, Randa, Yuan Tian, Mznah Al-Rodhaan, and Abdullah Al-Dhelaan. 2019. “Anomaly Detection over Differential Preserved Privacy in Online Social Networks.” PLOS ONE 14(4).

5. Hu, Zhaowei and Jing Yang. 2020. “Differential Privacy Protection Method Based on Published Trajectory Cross-Correlation Constraint.” PLOS ONE 15(8).

6. Ul Hassan, Muneeb, Mubashir Husain Rehmani, and Jinjun Chen. 2020. “Differential Privacy in Blockchain Technology: A Futuristic Approach.” Journal of Parallel and Distributed Computing 145:50–74.

7. Yilmax, Emre, Tianxi Ji, Erman Ayday, and Pan Li. 2021. Genomic Data Sharing under Dependent Local Differential Privacy 1–30.

8. Joseph, Matthew, Aaron Roth, Jonathan Ullman, and Bo Waggoner. 2020. “Local Differential Privacy for Evolving Data.” Journal of Privacy and Confidentiality 10(1).

9. Xiong, Xingxing, Shubo Liu, Dan Li, Zhaohui Cai, and Xiaoguang Niu. 2020. “A Comprehensive Survey on Local Differential Privacy.” Security and Communication Networks 2020:1–29.

10. Yang, Mengmeng, Lingjuan Lyu, Jun Zhao, Tianqing Zhu, and Kwok-Yan Lam. 2015. “Local Differential Privacy and Its Applications: A Comprehensive Survey.” Journal of Latex Class Files 14(8):1–25.

11. Zhao, Ping, Guanglin Zhang, Shaohua Wan, Gaoyang Liu, and Tariq Umer. 2019. “A Survey of Local Differential Privacy for Securing Internet of Vehicles.” The Journal of Supercomputing 76(11):8391–8412.

12. Imola, Jacob, Takao Murakami, and Kamalika Chaudhuri. 2020. “Locally Differentially Private Analysis of Graph Statistics.” USENIX Security 2021.

13. Garcelon, Evrard, Vianney Perchet , Ciara Pike-Burke, and Matteo Pirotta. 2020. “Local Differentially Private Regret Minimization in Reinforcement Learning.”

14. Cheu, Albert and Jonathan Ullman. 2020. “The Limits of Pan Privacy and Shuffle Privacy for Learning and Estimation.”

15. Ren, Wenbo, Xingyu Zhou, Jia Liu, and Ness B. Shroff. 2020. “Multi-Armed Bandits with Local Differential Privacy.”

16. Ucci, Daniele, Roberto Perdisci, Jaewoo Lee, and Mustaque Ahamad. 2020. “Building a Collaborative Phone Blacklisting System with Local Differential Privacy.”

17. Chen, Xiaoyu et al. 2020. “(Locally) Differentially Private Combinatorial Semi-Bandits.”

18. Chamikara, M. A. P., P. Bertok, I. Khalil, D. Liu, and S. Camtepe. 2020. “Privacy Preserving Face Recognition Utilizing Differential Privacy.” Computers & Security 97:101951.

19. Zhao, Yang et al. 2020. “Local Differential Privacy Based Federated Learning for Internet of Things.” IEEE Internet of Things Journal 1–33.

20. Acharya, Jayadev, Keith Bonawitz, Peter Kairouz, Daniel Ramage, and Ziteng Sun. 2020. “Context-Aware Local Differential Privacy.” NSF-CCF-1846300 1–24.