SMPC Task Decomposition: A Theory for Accelerating Secure Multi-party Computation Task

TL;DR

This paper introduces SMPC Task Decomposition (SMPCTD), a novel theory that divides large SMPC tasks into smaller sub-tasks to significantly reduce resource consumption and enable scalable secure computation for big data applications.

Contribution

The paper presents a new theoretical framework for decomposing SMPC tasks, improving efficiency and scalability in secure multi-party computation implementations.

Findings

Resource consumption drops sharply after decomposition

Time, memory, and communication are stabilized within a certain range

Decomposition enables processing larger datasets efficiently

Abstract

Today, we are in the era of big data, and data are becoming more and more important, especially private data. Secure Multi-party Computation (SMPC) technology enables parties to perform computing tasks without revealing original data. However, the underlying implementation of SMPC is too heavy, such as garbled circuit (GC) and oblivious transfer(OT). Every time a piece of data is added, the resources consumed by GC and OT will increase a lot. Therefore, it is unacceptable to process large-scale data in a single SMPC task. In this work, we propose a novel theory called SMPC Task Decomposition (SMPCTD), which can securely decompose a single SMPC task into multiple SMPC sub-tasks and multiple local tasks without leaking the original data. After decomposition, the computing time, memory and communication consumption drop sharply. We then decompose three machine learning (ML) SMPC tasks using our theory and implement them based on a hybrid protocol framework called ABY. Furthermore, we use incremental computation technique to expand the amount of data involved in these three SMPC tasks. The experimental results show that after decomposing these three SMPC tasks, the time, memory and communication consumption are not only greatly reduced, but also stabilized within a certain range.