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- Title
EPMA: Edge-Assisted Hierarchical Privacy-Preserving Multidimensional Data Aggregation Mechanism.
- Authors
Ma, Rong; Feng, Tao; Tian, Youliang; Xiong, Jinbo
- 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.
- Subjects
DATA privacy; EDGE computing; RIGHT of privacy; ELLIPTIC curves; SMART devices
- Publication
Mobile Networks & Applications, 2023, Vol 28, Issue 5, p1831
- ISSN
1383-469X
- Publication type
Article
- DOI
10.1007/s11036-023-02206-7