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

2024-08-02

arXiv.org

AI Summary

This paper addresses critical inefficiencies in multi-party secure collaborative analytics—specifically, low efficiency of Join-GroupBy-Aggregate (JGA) queries, high redundant join overhead, and expensive dynamic updates. To this end, we propose the first workload-agnostic materialized view framework tailored for JGA. Our core contributions are threefold: (1) an MPC-free incremental view maintenance mechanism; (2) a suite of specialized secure GroupBy-Aggregate protocols that bypass generic MPC bottlenecks; and (3) precomputed joins with view reuse to eliminate redundant join operations. Evaluated on real-world query workloads, our framework achieves up to 308.9× speedup over state-of-the-art approaches, while significantly reducing both communication and computation costs. The design rigorously preserves security guarantees under standard cryptographic assumptions, and supports efficient dynamic data updates—thus jointly optimizing security, performance, and adaptability.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Multiagent Systems: Mechanism Design
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• Secure collaborative analytics for join-group-aggregation queries
• Efficient materialized views for join and group-aggregation protocols
• Payload-independent view updates with minimal MPC overhead

Innovation

• Materialized views for join optimization
• Payload-independent view refreshing
• Novel group-aggregation protocols acceleration