Abstract
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.
Highlights
This paper addresses the problem that how to enable resource-constrained devices to outsource the Linear Regression machine learning algorithm to an untrustworthy cloud, in a way that guarantees privacy of the calculation and fairness for both parties
Linear Regression (LR) is a classical supervised learning algorithm, which is widely used to establish the relationship between the target variable and the input variable based on a trained model
The blockchain verifies the outputs which calculated by cloud server and guarantees the fairness
Summary
Hanlin Zhang is with the Business School, Qingdao University, Qingdao 266071, China. If cloud server performs computation task before outsourcer paying the services fee, the outsourcer might not pay after receiving the results. How to guarantee fairness for both the cloud server and the outsourcer is the last challenge. To address the above challenges, in this paper, we aim to investigate how to securely outsource the linear regression to an untrustworthy cloud, in a way that guarantees fairness for both parties. Based on the designed scheme, we implement the fair, secure outsourcing system on the Ethereum blockchain [3], which includes a verification system and a payment system. The rest of the paper is organized as follows: Section II introduces some essential preliminaries for the proposed scheme.
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