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首页> 外文期刊>International Journal of Innovative Computing Information and Control >A PROPOSAL OF PROFIT SHARING METHOD FOR SECURE MULTIPARTY COMPUTATION
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A PROPOSAL OF PROFIT SHARING METHOD FOR SECURE MULTIPARTY COMPUTATION

机译：安全多方计算利润分享方法的建议

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摘要

Many studies for secure computation using shared data on the cloud system are made to avoid secure risks being abused or leaked and to reduce computing cost. The secure multiparty computation (SMC) is one of these methods. There are two methods for constructing a machine learning (ML) based on SMC. One is a method of sharing learning data into several subsets and learning at each server. The other method is to divide the learning data itself and learn by using each server. In the latter, we have proposed learning methods of BP, k-means and fuzzy inference about SMC so far. Further, we proposed a learning method of SMC on Q-learning which is one of reinforcement learning methods, and showed its effectiveness in the previous paper. Though Q-learning is a learning method with excellent generalization ability, it is known that it takes much time to learn. On the other hand, the profit sharing (PS) method is known to have a shorter learning time than Q-learning. Therefore, it is desired that PS learning method for SMC is superior in learning time to Q-learning method for SMC. In this paper, we propose PS learning method on SMC and show its effectiveness.
机译：进行了许多使用云系统上的共享数据进行安全计算的研究，以避免滥用或泄漏安全风险并降低计算成本。安全多方计算（SMC）是这些方法之一。有两种基于SMC构造机器学习（ML）的方法。一种是将学习数据共享为几个子集并在每个服务器上进行学习的方法。另一种方法是划分学习数据本身，并使用每个服务器进行学习。在后者中，到目前为止，我们提出了关于SMC的BP，k均值和模糊推理的学习方法。此外，我们提出了一种基于Q学习的SMC学习方法，它是强化学习方法之一，并在先前的论文中证明了其有效性。尽管Q学习是一种具有出色泛化能力的学习方法，但众所周知它需要花费很多时间来学习。另一方面，已知的利润分享（PS）方法的学习时间比Q学习要短。因此，期望用于SMC的PS学习方法在学习时间上优于用于SMC的Q学习方法。在本文中，我们提出了在SMC上的PS学习方法并显示了其有效性。

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