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An Efficient Privacy-Preserving Data Aggregation Scheme for IoT

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Wireless Algorithms, Systems, and Applications (WASA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10874))

Abstract

Internet of Things (IoT) provides the most flexibility and convenience in our various daily applications as the IoT devices can improve efficiency, accuracy and economic benefit in addition to reduced human intervention. However, security and privacy challenges are also arising in IoT. To address this challenge, in this paper, we present a privacy-preserving data aggregation scheme for IoT to preserve privacy of customers. In our scheme, the IoT devices slice their actual data, keep one piece to themselves, and send the remaining pieces to other group devices via symmetric key. Then each IoT device adds the received slices and the held piece together to get immediate result, which should be sent to the server after the computation. Finally, analysis shows that our scheme can guarantee the integrity, confidentiality of IoT device’s data, and can resist external attack, internal attack and collusion attack and so on.

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Acknowledgments

Thank all the reviewers for their helpful comments. This project was partial supported by the National Key R&D Program of China (No. 2017YFB0802000), the National Natural Science Foundation of China (Grant No. 61702062, 61672118, 61672321, 61373027), Science and Technology Project of Guangdong Power Grid Co. Ltd. (GDKJXM20180250), and Chongqing Research Program of Basic Research and Frontier Technology (No. cstc2014jcyjA40030).

Author information

Authors and Affiliations

  1. School of Big Data and Software Engineering, Chongqing University, Chongqing, China

    Chunqiang Hu, Jin Luo & Yuwen Pu

  2. Key Laboratory of Dependable Service Computing in Cyber Physical Society, Ministry of Education, Chongqing University, Chongqing, China

    Chunqiang Hu & Tao Xiang

  3. School of Information Science and Engineering, Qufu Normal University, Jining, China

    Jiguo Yu

  4. Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd., Guangzhou, China

    Ruifeng Zhao

  5. College of Computer Science, Chongqing University, Chongqing, China

    Hongyu Huang & Tao Xiang

Authors
  1. Chunqiang Hu
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  2. Jin Luo
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  3. Yuwen Pu
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  4. Jiguo Yu
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  5. Ruifeng Zhao
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  6. Hongyu Huang
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  7. Tao Xiang
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Corresponding author

Correspondence to Chunqiang Hu .

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Editors and Affiliations

  1. University of South Florida, Tampa, Florida, USA

    Sriram Chellappan

  2. Virginia Commonwealth University, Richmond, Virginia, USA

    Wei Cheng

  3. Georgia State University, Atlanta, Georgia, USA

    Wei Li

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Hu, C. et al. (2018). An Efficient Privacy-Preserving Data Aggregation Scheme for IoT. In: Chellappan, S., Cheng, W., Li, W. (eds) Wireless Algorithms, Systems, and Applications. WASA 2018. Lecture Notes in Computer Science(), vol 10874. Springer, Cham. https://doi.org/10.1007/978-3-319-94268-1_14

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  • DOI: https://doi.org/10.1007/978-3-319-94268-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94267-4

  • Online ISBN: 978-3-319-94268-1

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