CryptEpsilon: Crypto-Assisted Differential Privacy on Untrusted Servers
Summary: CryptEpsilon enables central-model DP accuracy with two non-colluding servers on encrypted data. Analysts author DP programs that are automatically compiled into secure protocols, delivering broad expressibility and practical performance via noise-aware optimizations on real datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Amrita Roy Chowdhury (University of Wisconsin)
- 2. Chenghong Wang (Duke University)
- 3. Xi He (University of Waterloo)
- 4. Ashwin Machanavajjhala (Duke University)
- 5. Somesh Jha (University of Wisconsin)
BibTeX Citation
@inproceedings{chowdhury_sigmod20,
title = {{CryptEpsilon: Crypto-Assisted Differential Privacy on Untrusted Servers}},
author = {Chowdhury, Amrita Roy and Wang, Chenghong and He, Xi and Machanavajjhala, Ashwin and Jha, Somesh},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3380596},
url = {https://dl.acm.org/doi/10.1145/3318464.3380596},
year = {2020}
}
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