Many researches on privacy preserving data mining have been done. Privacy preserving data mining can be achieved in various ways by use of randomization techniques, cryptographic algorithms, anonymization methods, etc. Further, in order to increase the security of data mining, secure multiparty computation (SMC) has been introduced. Most of works in SMC are developed on applying the model of SMC on different data distributions such as vertically, horizontally and arbitrarily partitioned data. Another type of SMC with sharing data itself to each party attracts attention, and some studies have been done. A simple method to share data was proposed and it was applied to statistical computation. However, for SMC, complicated computation such as data mining has never been proposed. In the previous paper, we proposed a BP learning for SMC and showed the effectiveness of it. In this paper, we propose clustering methods such as k-means and NG for SMC and show the effectiveness in numerical simulation.
Many studies have been done with the security of cloud computing. Though data encryption is a typical approach, high computing complexity for encryption and decryption of data is needed. Therefore, safe system for distributed processing with secure data attracts attention, and a lot of studies have been done. Secure multiparty computation (SMC) is one of these methods. Specifically, two learning methods for machine learning (ML) with SMC are known. One is to divide learning data into several subsets and perform learning. The other is to divide each item of learning data and perform learning. So far, most of works for ML with SMC are ones with supervised and unsupervised learning such as BP and K-means methods. It seems that there does not exist any studies for reinforcement learning (RL) with SMC. This paper proposes learning methods with SMC for Q-learning which is one of typical methods for RL. The effectiveness of proposed methods is shown by numerical simulation for the maze problem.
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