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EPMA: Edge-Assisted Hierarchical Privacy-Preserving Multidimensional Data Aggregation Mechanism

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Abstract

Most current data aggregation schemes treat data collected from smart devices as one-dimensional data and only support the aggregation of homogeneous types of data, but not the aggregation of multidimensional heterogeneous types of data. To address this problem, this paper proposes an edge-assisted hierarchical privacy-preserving multidimensional data aggregation mechanism (EPMA). In this mechanism, using a hierarchical aggregation framework assisted by edge computing, we propose a multi-region multidimensional data aggregation scheme that utilizes the homomorphic Paillier algorithm and Horner’s law to achieve privacy aggregation while effectively reducing computation and communication overhead. It provides strong support for secure and efficient multidimensional data collection and communication. In particular, Horner’s law allows different fine-grained aggregation results to be parsed from the aggregated ciphertexts, providing flexibility to meet different data analysis needs. In addition, we propose an efficient signature authentication method adopting lightweight elliptic curve encryption algorithms and bulk authentication techniques to ensure data integrity and identity validity. Finally, the security analysis proves that the EPMA mechanism is secure, and the theoretical analysis and simulation experiments illustrate that the EPMA mechanism has lower computational cost compared with other mechanisms and is more suitable for practical industrial application scenarios.

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Funding

This work is supported by the National Natural Science Foundation of China (Grant No. 62162039, 61762060).

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Authors and Affiliations

  1. College of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China

    Rong Ma & Tao Feng

  2. College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China

    Youliang Tian

  3. College of Computer and Cyber Security, Fujian Normal University, Fuzhou, 350117, China

    Jinbo Xiong

Authors
  1. Rong Ma
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  2. Tao Feng
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  3. Youliang Tian
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  4. Jinbo Xiong
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Contributions

Rong Ma and Tao Feng contributed to the conception of the study and wrote the manuscript; Youliang Tian helped perform the analysis with constructive discussions; Jinbo Xiong contributed significantly to analysis and manuscript preparation.

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Correspondence to Rong Ma.

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Ma, R., Feng, T., Tian, Y. et al. EPMA: Edge-Assisted Hierarchical Privacy-Preserving Multidimensional Data Aggregation Mechanism. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02206-7

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  • DOI: https://doi.org/10.1007/s11036-023-02206-7

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