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MSDA: multi-subset data aggregation scheme without trusted third party

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Abstract

Data aggregation has been widely researched to address the privacy concern when data is published, meanwhile, data aggregation only obtains the sum or average in an area. In reality, more fine-grained data brings more value for data consumers, such as more accurate management, dynamic price-adjusting in the grid system, etc. In this paper, a multi-subset data aggregation scheme for the smart grid is proposed without a trusted third party, in which the control center collects the number of users in different subsets, and obtains the sum of electricity consumption in each subset, meantime individual user’s data privacy is still preserved. In addition, the dynamic and flexible user management mechanism is guaranteed with the secret key negotiation process among users. The analysis shows MSDA not only protects users’ privacy to resist various attacks but also achieves more functionality such as multi-subset aggregation, no reliance on any trusted third party, dynamicity. And performance evaluation demonstrates that MSDA is efficient and practical in terms of communication and computation overhead.

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Acknowledgements

This work was supported partly by the National Natural Science Foundation of China (Grant Nos. 61162016, 62072133, U1811264, U1711263, 61966009), the Natural Science Foundation of Guangxi Province (2018GXNSFDA281040, 2018GXNSFDA281045), and the Innovation Project of Guangxi Graduate Education (YCBZ2020062).

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Authors and Affiliations

  1. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Zhixin Zeng, Xiaodi Wang, Yining Liu & Liang Chang

Authors
  1. Zhixin Zeng
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  2. Xiaodi Wang
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  3. Yining Liu
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  4. Liang Chang
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Corresponding author

Correspondence to Liang Chang.

Additional information

Zhixin Zeng received the BE degree in software engineering from Xiamen University of Technology, China in 2017. He is currently a PhD candidate of Guilin University of Electronic Technology, China. His research interests include information security, data privacy and machine learning.

Xiaodi Wang received the BE degree in information security from Guilin University of Electronic Technology, China in 2017. She is currently a master student of Guilin University of Electronic Technology, China. Her research interests include information security and data privacy.

Yining Liu received BS degree in applied mathematics from Information Engineering University, China in 1995, the ME degree in computer software and theory from Huazhong University of Science and Technology, China in 2003, and PhD degree in mathematics from Hubei University, China in 2007. He is currently a professor with school of Computer and Information Security, Guilin University of Electronic Technology, China. His research interests include the information security protocol and data privacy.

Liang Chang received the PhD degree in computer science from the Institute of Computing Technology, Chinese Academy of Sciences, China in 2008. He is currently a Professor with the School of Computer Science and Information Security, Guilin University of Electronic Technology, China. His research interests include trusted software and security protocol.

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Zeng, Z., Wang, X., Liu, Y. et al. MSDA: multi-subset data aggregation scheme without trusted third party. Front. Comput. Sci. 16, 161808 (2022). https://doi.org/10.1007/s11704-021-0316-x

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  • DOI: https://doi.org/10.1007/s11704-021-0316-x

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