  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.
