点击次数：

所属单位：计算机科学与技术学院/人工智能学院/软件学院

发表刊物：FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE

关键字：Cloud computing Secure outsourcing Linear regression Verification Data perturbation

摘要：In this paper, we study the problem of efficiently outsourcing large-scale linear regression of a customer to the public cloud while preserving the privacy of the customer's input and output results. To reduce the customer's computation costs, existing schemes generally use diagonal matrix multiplication to encrypt the input data. While such approaches are efficient, there are potential security limitations. For example, in this paper we reveal previously unknown limitations in the scheme of Chen et al. (2014). We then present a novel method to generate random dense matrices, and a new secure solution for outsourcing linear regression to cloud. In our proposed approach, we perturb the customer's input/output by adding random numbers and multiplying our constructed random dense matrices. A comparative summary demonstrates that the proposed approach has a stronger level of security, without incurring additional computation complexity. We also demonstrate that our constructed dense matrices can be utilized to efficiently enhance the security of outsourcing scheme for other large-scale matrix operations, including linear equation system and determinant computation. (C) 2017 Elsevier B.V. All rights reserved.

ISSN号：0167-739X

是否译文：否

发表时间：2018-04-01

合写作者：Lu Zhou,Choo, Kim-Kwang Raymond

通讯作者：朱友文