摘要
随着工业5.0的推广,物联网需要对运行数据进行实时采集和上传存储。为了更精确地描述和分析物联网工作状态,需要采集高精度实时数据。然而物联网不同类型数据的混合存储会降低数据分析效率,为了提高混合存储环境中的数据分析效率,需在数据上传过程中对数据进行分流来实现数据的分类存储。传统的数据分流方法只能对明文数据依据其来源来实现分流,而明文数据的来源信息会泄露设备的身份隐私。因此,如何在不泄露隐私的基础上,通过密文分流实现物联网数据的分类存储,成了物联网数据安全管理亟待解决的问题。文中提出一个隐私保护的物联网数据筛选方案,在保障内容和设备身份隐私的基础上,通过数据发送设备的身份生成筛选陷门来设定中继节点设备数据筛选规则,在数据上传阶段对数据进行筛选分流,将混合的异源数据按数据来源分类为同源数据进行分别存储,为后期的数据访问控制及分析提供服务支撑。实验结果表明,所提方案比同类型的方案执行效率更高。
With the development of industry 5.0,the operational data need to be collected and uploaded in real time in the practical Internet of Things(IoT).To describe and analyze the working state of the IoT more precisely,high accurate and real-time data are required.Then,in practical applications,many different types of IoT data are stored together without classifying,which could reduce the efficiency of data analysis.In order to improve the efficiency of data analysis in the hybrid data storage environment,it is necessary to use the method of data shunting in the process of data upload to realize the classified storage of data.However,the traditional data shunting method shunts the plaintext data according to its source identity,during which the source information on the plaintext data will leak the identity and privacy of the IoT devices.Therefore,how to realize the classified storage of these IoT data through the data shunting without revealing the privacy has become an urgent problem to be solved in the security management of the IoT data.In this paper,a new privacy-preserving IoT data filtering scheme is proposed.On the basis of maintaining the context and device identity privacy,each data filtering rule is set by a filtering trapdoor,which is computed from the identity of the data source device.Then,the data can be classified and routed by the relay nodes following the given rules in the data uploading phase,from which the heterologous data can be classified and the homologous data are stored together,which can help further data access control and data analysis.Experiment results show that our scheme is efficient and practical.
作者
周让
张小松
汪小芬
李冬芬
陈涛
张晓均
ZHOU Rang;ZHANG Xiaosong;WANG Xiaofen;LI Dongfen;CHEN Tao;ZHANG Xiaojun(College of Computer Science and Cyber Security,Chengdu University of Technology,Chengdu 610059,China;School of Computer Science and Engineering,University of Electronic Science and Technology of China,Chengdu 611731,China;Cyberspace Security Research Center,Peng Cheng Laboratory,Shenzhen 518055,China;School of Computer Science,Southwest Petroleum University,Chengdu 610500,China)
出处
《西安电子科技大学学报》
EI
CAS
CSCD
北大核心
2023年第4期45-53,共9页
Journal of Xidian University
基金
国家自然科学基金(62102048,U19A2066,62172060,61902327)
四川省自然科学基金(2023NSFSC1399)
四川省重点研发计划(2022YFG0316)。
关键词
物联网
数据筛选
筛选陷门
筛选标签
设备身份隐私
Internet of Things
data filtering
filtering trapdoor
filtering index
device identity privacy
