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.

翻译：当下我们正处于大数据时代，数据尤其是隐私数据的重要性日益凸显。安全多方计算（SMPC）技术使各方能够在无需披露原始数据的前提下执行计算任务。然而，SMPC的底层实现机制（如乱码电路GC和不经意传输OT）过于繁重。每当数据量增加时，GC和OT消耗的资源会大幅增长。因此，在单个SMPC任务中处理大规模数据是难以接受的。本研究提出一项名为"SMPC任务分解"（SMPCTD）的新理论，该理论可在不泄露原始数据的前提下，将单个SMPC任务安全分解为多个SMPC子任务与多个本地任务。分解后，计算耗时、内存消耗及通信开销均显著下降。我们运用该理论分解了三个机器学习（ML）类SMPC任务，并基于名为ABY的混合协议框架实现。进一步采用增量计算技术扩展了这三个SMPC任务所涉及的数据规模。实验结果表明：经分解后，这三个SMPC任务的时间、内存及通信消耗不仅大幅降低，且稳定在特定范围内。