Differential privacy is the de facto standard privacy notion for privacy-preserving data analysis and publishing. Roughly speaking, differential privacy means the output distributions from an analysis will be essentially the same, regardless whether any individual joins, or refrains from joining, the dataset. This is usually achieved by adding a pre-determined amount of randomness, or “noise”, into a computation performed on a dataset. The noise makes the output insensitive to any single record in the dataset, so that every individual in the dataset can plausibly deny that their data was included.
My research on differential privacy focuses on developing novel mechanisms with differential privacy guarantee, while striking a balance among privacy, utility and efficiency.
Related publications
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TIFS
Distributed Differential Privacy via Shuffling Versus Aggregation:
A Curious Study
Yu Wei, Jingyu Jia, Yuduo Wu, Changhui Hu,
Changyu Dong, Zheli Liu, Xiaofeng Chen, Yun Peng, and Shaowei Wang
IEEE Trans. Inf. Forensics Secur., 2024
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Inf. Sci.
The influence of explanation designs on user understanding differential privacy and making data-sharing decision
Zikai Alex Wen, Jingyu Jia, Hongyang Yan, Yaxing Yao, Zheli Liu, and Changyu Dong
Information Sciences, 2023
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Inf. Sci.
Total variation distance privacy: Accurately measuring inference attacks
and improving utility
Jingyu Jia, Chang Tan, Zhewei Liu, Xinhao Li, Zheli Liu, Siyi Lv, and Changyu Dong
Information Sciences, 2023
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IJIS
Understanding adaptive gradient clipping in DP-SGD, empirically
Guanbiao Lin, Hongyang Yan, Guang Kou, Teng Huang, Shiyu Peng, Yingying Zhang, and Changyu Dong
Int. J. Intell. Syst., 2022
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New Differential Privacy Communication Pipeline and Design Framework (Poster)
Jingyu Jia, Zikai Wen, Zheli Liu, and Changyu Dong
In 18th Symposium on Usable Privacy and Security (SOUPS 2022), 2022
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TPDS
Differentially Private Byzantine-robust Federated Learning
Xu Ma, Xiaoqian Sun, Yuduo Wu, Zheli Liu, Xiaofeng Chen, and Changyu Dong
IEEE Trans. Parallel Distributed Syst., 2022
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Differentially Private String Sanitization for Frequency-Based Mining
Tasks
Huiping Chen,
Changyu Dong, Liyue Fan, Grigorios Loukides, Solon P. Pissis, and Leen Stougie
In IEEE International Conference on Data Mining, 2021
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How to Make Private Distributed Cardinality Estimation Practical,
and Get Differential Privacy for Free
Changhui Hu, Jin Li, Zheli Liu, Xiaojie Guo, Yu Wei, Xuan Guang, Grigorios Loukides, and Changyu Dong
In 30th USENIX Security Symposium, 2021