MapComp: A Secure View-based Collaborative Analytics Framework for Join-Group-Aggregation

TL;DR

MapComp introduces a view-based framework that significantly enhances the efficiency of secure join-group-aggregation queries in collaborative data analytics, enabling scalable, real-time privacy-preserving analysis with substantial performance gains.

Contribution

It is the first to utilize materialized views for accelerating secure JGA queries, offering novel protocols that outperform existing solutions in efficiency and update handling.

Findings

Achieves up to 308.9x efficiency improvement over baseline.

Develops novel protocols surpassing prior oblivious sorting solutions by 1140.5x.

Effectively handles continuous data updates with minimal MPC overhead.

Abstract

Join-group-aggregation (JGA) queries are fundamental to data analytics, yet executing them collaboratively across different parties poses significant privacy risks. Secure multi-party computation (MPC) offers a cryptographic solution. However, existing MPC-based JGA approaches consider only a one-time query paradigm and suffer from significant performance bottlenecks. It executes expensive join operations from scratch across multiple queries and employs inefficient group-aggregation (GA) protocols, both of which hinder their practical use for scalable, real-time analysis. This paper introduces MapComp, a novel view-based framework to facilitate JGA queries for secure collaborative analytics. Through specially crafted materialized views for join and novel design of GA protocols, MapComp removes duplicate join workload and expedites subsequent GA, improving the efficiency of JGA query execution. To address the challenge of continuous data updates, our materialized view offers payload-independence feature and provides significant efficiency improvements in view refreshing with free MPC overhead. This feature, on the other hand, also allows further acceleration for GA, where we devise multiple novel protocols that outperform prior works. Notably, our work represents the first endeavor to expedite secure collaborative JGA queries using materialized views. Our rigorous experiments demonstrate a significant advantage of MapComp, achieving up to a 308.9x improvement in efficiency over the baseline in real-world query simulations. Moreover, our optimized GA protocols achieve up to a 1140.5x improvement compared to prior oblivious sorting-based solutions.