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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 6, 2023.
Abstract: With the large-scale deployment of smart grids, the scheme of smart grid data aggregation has gradually enriched in recent years. Based on the principle of protecting user privacy, existing schemes usually choose to introduce a trusted third party (TTP) to participate in the collaboration. However, this also increases the risk of privacy exposure as the attacker can target the TTP which provides services to smart grid operators. In addition, many existing schemes do not take into account the operational requirements of smart meters in case of failure. Furthermore, some schemes ignore the control center’s demand for analyzing multi-dimensional data, which causes a lot of inconvenience in actual operation. Therefore, a fault-tolerant multi-dimensional data aggregation scheme is proposed in this paper. We have constructed a scheme without TTP participation in collaboration, and also meet the following two requirements. The scheme not only ensures the normal operation of the system when the smart meter fails but also meets the requirements of the control center for multi-dimensional data analysis. Security analysis shows that the proposed scheme can resist external attack, internal attack, and collusion attack. The experimental results show that the proposed scheme improves the fault tolerance and reduces the computational cost compared with the existing schemes.
Yong Chen, Feng Wang, Li Xu and Zhongming Huang, “Multi-dimensional Data Aggregation Scheme Supporting Fault-Tolerant Mechanism in Smart Grid” International Journal of Advanced Computer Science and Applications(IJACSA), 14(6), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01406120
@article{Chen2023,
title = {Multi-dimensional Data Aggregation Scheme Supporting Fault-Tolerant Mechanism in Smart Grid},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01406120},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01406120},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {6},
author = {Yong Chen and Feng Wang and Li Xu and Zhongming Huang}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.