Paper 2023/675
Efficient and Secure Quantile Aggregation of Private Data Streams
Abstract
Computing the quantile of a massive data stream has been a crucial task in networking and data management. However, existing solutions assume a centralized model where one data owner has access to all data. In this paper, we put forward a study of secure quantile aggregation between private data streams, where data streams owned by different parties would like to obtain a quantile of the union of their data without revealing anything else about their inputs. To this end, we designed efficient cryptographic protocols that are secure in the semi-honest setting as well as the malicious setting. By incorporating differential privacy, we further improve the efficiency by 1.1× to 73.1×. We implemented our protocol, which shows practical efficiency to aggregate real-world data streams efficiently.
Metadata
- Available format(s)
-
PDF
- Category
- Applications
- Publication info
- Published elsewhere. IEEE Transactions on Information Forensics & Security
- DOI
- 10.1109/TIFS.2023.3272775
- Keywords
- quantile aggregationmulti-party computationdifferential privacy
- Contact author(s)
-
lanxiao @ scu edu cn
jinhongjian545 @ 163 com
guohtech @ foxmail com
wangxiao1254 @ gmail com - History
- 2023-05-15: approved
- 2023-05-12: received
- See all versions
- Short URL
- https://ia.cr/2023/675
- License
-
CC BY-NC
BibTeX
@misc{cryptoeprint:2023/675,
author = {Xiao Lan and Hongjian Jin and Hui Guo and Xiao Wang},
title = {Efficient and Secure Quantile Aggregation of Private Data Streams},
howpublished = {Cryptology {ePrint} Archive, Paper 2023/675},
year = {2023},
doi = {10.1109/TIFS.2023.3272775},
url = {https://eprint.iacr.org/2023/675}
}