Simplified security learning using vertically partitioned data with IoT

Abstract

Edge (or fog) computing is known as a method for improving the conventional cloud system. The basic idea is to consider a system that places edges (servers) between the cloud and the terminals (things). Then, how should machine learning be realized on the edge system? Fast and secure learning methods are desired for machine learning. Secure systems using distributed processing have attracted attention. SMC (Secure Multiparty Computation) is one of the typical models to realize secure learning. Horizontally and vertically partitioned data are known for SMC. The latter is a method consisting of dividing the dataset into element-separated subsets. It is desired to develop a method for directly executing learning using element-separated subsets. Vertically partitioned data (VPD) is considered to be a data structure that realizes such learning. In the previous papers, we proposed learning methods for BP (Back Propagation) and NG (Neural Gas) using VPD. There, we did not consider about the amount of data transferred between servers. In this paper, simplified learning methods that eliminate wasteful data transfer compared to the method in the previous papers are proposed, and its effectiveness is shown. That is, the data transfer from the central server to each server was reduced to 1/L, where L is the number of training data.