Neural Gas and K-Means Methods Improving the Computational Complexity for Secure Distributed Processing

Abstract

To realize a super-smart society, it is necessary to aim for advanced integration of cyberspace and physical space (real society). Artificial Intelligence (AI) analysis of big data will bring effective information that meets the needs of individuals and companies to real society more quickly. On the other hand, to build a safe and secure society, it is important to develop AI methods that protect the privacy of big data in cyberspace. However, there is a little-known method that satisfies both data confidentiality and utilization of the learning method. Therefore, the authors proposed a learning method for secure distributed processing using decomposition data. This method has higher confidentiality and utilization than the conventional method, but the increase in computational complexity due to distributed processing is a problem. In the previous paper, the authors proposed the Back Propagation (BP) method to solve this problem. In this paper, we apply this method to the Neural Gas (NG) and k-means methods for secure distributed processing, which are unsupervised learning, and show its effectiveness.