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Privacy-Preserving Lightweight Data Monitoring in Internet of Things Environments

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Abstract

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.

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Authors and Affiliations

  1. Guangxi Key Laboratory of Cryptography and Information Security, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China

    Meng Zhao, Yong Ding & Yujue Wang

  2. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

    Meng Zhao

  3. Cyberspace Security Research Center, Peng Cheng Laboratory, Shenzhen, 518055, China

    Yong Ding

  4. School of Cyber Science and Technology, Beihang University, Beijing, 100191, China

    Qianhong Wu

  5. State Key Laboratory of Cryptology, P. O. Box 5159, Beijing, 100878, China

    Qianhong Wu

  6. Key Laboratory of Data Engineering and Knowledge Engineering, Ministry of Education, School of Information, Renmin University of China, Beijing, 100872, China

    Bo Qin

  7. China Electronics Standardization Institute, Beijing, 100007, China

    Kefeng Fan

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  1. Meng Zhao
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  2. Yong Ding
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  3. Qianhong Wu
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  4. Yujue Wang
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  5. Bo Qin
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Correspondence to Yong Ding.

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This article is supported in part by the National Key R&D Program of China through project 2017YFB1400700, the National Natural Science Foundation of China under Projects 61862012, 61772150, 61772538, 61672083, 61862011, 61962012, 61972019, 61932011 and 91646203, the Guangxi Key R&D Program under Project AB17195025, the Guangxi Natural Science Foundation under Grants 2018GXNSFDA281054, 2018GXNSFAA281232, 2019GXNSFFA245015, 2019GXNSFGA245004 and AD19245048, the Peng Cheng Laboratory Project of Guangdong Province PCL2018KP004, the National Cryptography Development Fund through project MMJJ20170106, and the foundation of Science and Technology on Information Assurance Laboratory through project 61421120305162112006.

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Zhao, M., Ding, Y., Wu, Q. et al. Privacy-Preserving Lightweight Data Monitoring in Internet of Things Environments. Wireless Pers Commun 116, 1765–1783 (2021). https://doi.org/10.1007/s11277-020-07760-x

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  • DOI: https://doi.org/10.1007/s11277-020-07760-x

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