This paper introduces MapComp, a novel view-based framework to facilitate join-group-aggregation (JGA) queries for secure collaborative analytics. Through specially crafted materialized views for join and novel design of group-aggregation (GA) protocols, MapComp removes duplicated join workload and expedites subsequent GA, improving the efficiency of JGA query execution. To support continuous data updates, our materialized view offers payload-independence feature and brings in significant efficiency improvement of view refreshing with free MPC overhead. This feature also allows further acceleration for GA, where we devise multiple novel protocols that outperform prior works. Our rigorous experiments demonstrate a significant advantage of MapComp, achieving up to a 308.9x efficiency improvement compared to the baseline in the real-world query simulation.
View on arXiv@article{peng2025_2408.01246,
title={ MapComp: A Secure View-based Collaborative Analytics Framework for Join-Group-Aggregation },
author={ Xinyu Peng and Feng Han and Li Peng and Weiran Liu and Zheng Yan and Kai Kang and Xinyuan Zhang and Guoxing Wei and Jianling Sun and Jinfei Liu and Lin Qu },
journal={arXiv preprint arXiv:2408.01246},
year={ 2025 }
}