Welcome to the resource topic for 2023/675
Title:
Efficient and Secure Quantile Aggregation of Private Data Streams
Authors: Xiao Lan, Hongjian Jin, Hui Guo, Xiao Wang
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.
ePrint: https://eprint.iacr.org/2023/675
See all topics related to this paper.
Feel free to post resources that are related to this paper below.
Example resources include: implementations, explanation materials, talks, slides, links to previous discussions on other websites.
For more information, see the rules for Resource Topics .