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Privacy-preserving and fault-tolerant aggregation of time-series data without TA

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Abstract

The increase in popularity and usage of the Internet of Things (IoT) applications, along with big data, has highlighted time-series data aggregation. Data is continuously and periodically generated in a time-series scenario and then transported to the aggregator for analysis. Data aggregation is a helpful operation to preprocess data, where a group of users sense the time-series data. However, some security and privacy issues still need to be solved. Many traditional privacy-preserving solutions cannot support fault tolerance, a vital feature in time-series scenarios. Moreover, a trusted authority is difficult to build in the real world. This paper proposes a privacy-preserving time-series data aggregation scheme without TA. The proposed scheme can also compute arbitrary aggregate functions and achieve fault tolerance for enhancing data aggregation’s reliability and scalability. Security analysis demonstrates that our proposed scheme achieves forward secrecy and fault tolerance. We also conduct thorough experiments based on a simulated data aggregation scenario to show the scheme’s computation and communication efficiency.

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Data availability

The datasets used or analysed during the current study are available.

Notes

  1. http://www.indiaenvironmentportal.org.in/content/412608/smarter2030-ict-solutions-for-21st-century-challenges/

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Acknowledgements

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Funding

This research is supported by the National Key R&D Program of China (Grant Nos.2021YFB2700500, 2021YFB2700502) and the National Natural Science Foundation of China (Grant Nos. 61972037, U1804263).

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Authors and Affiliations

  1. School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Chang Xu, Liehuang Zhu & Can Zhang

  2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Run Yin & Kashif Sharif

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  1. Chang Xu
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  2. Run Yin
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  3. Liehuang Zhu
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  4. Can Zhang
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  5. Kashif Sharif
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Contributions

C.X. contributed to the manuscript’s idea, formal analysis, and reviewing and editing the manuscript. R.Y. designed the scheme, conducted the experiments, gave formal analysis, and wrote the manuscript. L.Z. contributed to the manuscript’s idea, scheme design, and reviewing and editing the manuscript. C.Z. contributed to the manuscript’s idea and reviewing and editing the manuscript. K.S. contributed to the scheme design and formal analysis.

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Correspondence to Chang Xu.

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Xu, C., Yin, R., Zhu, L. et al. Privacy-preserving and fault-tolerant aggregation of time-series data without TA. Peer-to-Peer Netw. Appl. 16, 358–367 (2023). https://doi.org/10.1007/s12083-022-01420-6

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