计算机与数字工程2019，Vol.47Issue(1)：157-160,230,5.DOI:10.3969/j.issn.1672-9722.2019.01.034
云计算环境下安全的极限学习机外包优化部署机制∗
Optimization Mechanism for Secure Outsourcing Extreme Learning Machine in Cloud Computing
摘要
Abstract
To address the challenge of big data,abundant computing resource and efficient machine learning algorithm are necessary for processing and analyze big data. This paper focuses on outsourcing Extreme Learning Machine(ELM)in cloud,and presents an optimization mechanism in order to improve training speed of ELM,as well as ensure the confidentiality and privacy of input and output. The proposed optimization mechanism explicitly divides ELM into two parts,a public part and a private part. The private part is executed locally,consisting of generation of random parameters,calculation of output matrix of hidden layer,interme?diate matrix,and output weight matrix. The public part is executed in cloud that is mainly responsible for calculating the inverse of the intermediate matrix,which is the heaviest operation computationally. The inverse also serves as the correctness and soundness proof in result verification. We analyze the confidentiality and communication cost theoretically and the experimental results demon?strate that the proposed mechanisms can effectively release customers from heavy computations.关键词
极限学习机/云计算/计算外包/数据安全/隐私保护/结果验证Key words
Key Words cloud computing/extreme learning machine/computing outsourcing/data security/privacy-preserving/result verification分类
信息技术与安全科学引用本文复制引用
林加润,殷建平,张晓峰,蔡志平,明月伟..云计算环境下安全的极限学习机外包优化部署机制∗[J].计算机与数字工程,2019,47(1):157-160,230,5.基金项目
国家自然科学基金项目(编号:61672528,61303189,61232016,61170287,61363071) （编号:61672528,61303189,61232016,61170287,61363071）
海南省自然科学基金(编号:617048)资助. （编号:617048）
