Privacy-Preserving Lightweight Data Monitoring in Internet of Things Environments

被引:0
|
作者
Meng Zhao
Yong Ding
Qianhong Wu
Yujue Wang
Bo Qin
Kefeng Fan
机构
[1] Guilin University of Electronic Technology,Guangxi Key Laboratory of Cryptography and Information Security, School of Computer Science and Information Security
[2] Chinese Academy of Sciences,State Key Laboratory of Information Security, Institute of Information Engineering
[3] Cyberspace Security Research Center,School of Cyber Science and Technology
[4] Peng Cheng Laboratory,Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, School of Information
[5] Beihang University,undefined
[6] State Key Laboratory of Cryptology,undefined
[7] Renmin University of China,undefined
[8] China Electronics Standardization Institute,undefined
来源
关键词
Data monitoring; Privacy protection; Internet of Things; Delegated computing;
D O I
暂无
中图分类号
学科分类号
摘要
The fast development of Internet of Things (IoT) has shown that it becomes one of the most popular techniques. In the IoT paradigm, ubiquitous sensors and smart devices can be interconnected to collect various status data and share with others. When deployed in an environment status monitoring system, distributed sensors may be requested to periodically report real-time data. The large-scale data would make the system controller unable to process in time. In this case, a third-party server can be engaged to conduct most of monitoring work, where sensors direct report to the server to generate intermediate monitoring results for the system controller. However, the server may be curious about the contents of outsourced system standing queries, data vectors of sensors, and monitoring results. In addition, due to the limited computing resources of distributed sensors, existing cryptographic solutions are not applicable to such monitoring scenario. To address these issues, this paper proposes a lightweight server-aided data monitoring scheme (SIM). Thorough efficiency and privacy analysis confirm the practicality of the proposed SIM scheme. Moreover, this paper extends Lu et al.’s privacy-preserving cosine similarity computing protocol in the two-party setting in big data environment to support computing on any dimensional data, without incurring expensive calculations.
引用
收藏
页码:1765 / 1783
页数:18
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