秘匿分散処理による機械学習法の計算量削減

抄録

To realize a super-smart society, it is necessary to aim for advanced integration of cyberspace and physical space (real society). AI analysis of big data will bring effective information that meets the needs of individuals and companies to the real society more quickly. On the other hand, to build a safe and secure society, it is important to develop AI analysis methods that protect the privacy of big data in cyberspace. However, there is no known method that satisfies both data confidentiality and usability of the learning method at a high level. Therefore, the authors have proposed a distributed AI method using secret decomposition data. This method has higher confidentiality and usability than conventional methods, but the increase in computational complexity due to distributed processing is a problem. In this paper, we propose a new learning method to solve this problem. In particular, we apply this method to the Neural-Gas(NG) algorithm, which is unsupervised learning, and show its effectiveness.