文章基本信息

标题：Proposal of Fast and Secure Clustering Methods for IoT
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作者：Hirofumi Miyajima ; Hiromi Miyajima ; Norio Shiratori 等
期刊名称：Lecture Notes in Engineering and Computer Science
印刷版ISSN：2078-0958
电子版ISSN：2078-0966
出版年度：2019
卷号：2239
页码：1-6
出版社：Newswood and International Association of Engineers
摘要：Cloud (computing) system has been widely used as one of ICTs. However, as the number of terminals connected to it increases, the limit of the capability is also becoming apparent. The limit of its capability leads to the delay of significant processing time. In order to improve this, the edge (or fog) computing system has been proposed. In the conventional cloud system, a terminal sends all data to the cloud and the cloud returns the computation result to the terminal (or thing) directly connected to it. On the other hand, in the edge (computing) system, a plural of servers called edges are assigned between the cloud and the terminal (or thing). In the system, there are two groups of servers for cloud and edge. Heavy and normal tasks are processed in cloud and edge servers, respectively. Then, let us consider about machine learning in cloud or edge system. The purpose of learning is to find out the relationship (information) lurking in from the collected data. That is, a system with several parameters is assumed and estimated by repeatedly updating the parameters with learning data. Further, there is the problem of the security for learning data. How can we build cloud system to avoid such risk? Secure multiparty computation (SMC) is known as one method realizing secure computation. Many studies on learning methods based on SMC have also been proposed in the cloud system. Then, what kind of learning method is suitable for edge system based on SMC? In this paper, we will propose Neural Gas (NG) algorithms to realize fast and secure processing on edge computing for clustering and classification problems, and show the effectiveness of the proposed methods in numerical simulations.
关键词：IoT; Machine learning; Security; Batch learning;; Clustering; Classification problem; Neural Gas.