Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.
翻译:线性回归(LR)是一种古典机器学习算法,在网络物理社会系统(CPSS)中有许多应用,用以塑造和简化我们的生活、工作和交流方式。本文件侧重于在使用线性回归时对CPSS进行的数据分析。LR的培训过程耗时,因为它涉及复杂的矩阵操作,特别是当它在CPSS中获得大规模培训数据集时。因此,如何使装置能够高效率地开展线性回归培训过程是非常重要的。为了解决这个问题,我们在本文件中提出了一个安全、可核查和公平的方法,将LR外包给一个不可靠的云性服务器。在拟议的办法中,计算投入/产出是模糊不清的,因此敏感信息的隐私不受云性服务器的干扰。与此同时,云性服务器的计算结果是可以核实的。此外,由块链保证公平性,确保云只有在正确履行外包工作量时才得到支付。基于所提出的办法,我们利用了Etheinum块链上的公平、安全的外包系统。我们分析了我们提出的所有理论和理论性计划是有效的。