Netinfo Security ›› 2021, Vol. 21 ›› Issue (11): 28-39.doi: 10.3969/j.issn.1671-1122.2021.11.004

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Privacy-preserving Data Aggregation with Fine Grained Access Control for Smart Grid

XIA Zhe1(), LUO Bin2, XU Guibin2, XIAO Xinxiu2   

  1. 1. School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan 430071, China
    2. Hubei Huazhong Electric Power Technology Development Co. Ltd., Wuhan 430207, China
  • Received:2021-07-08 Online:2021-11-10 Published:2021-11-24
  • Contact: XIA Zhe E-mail:xiazhe@whut.edu.cn

Abstract:

Smart grid enables dynamic power allocation and intelligent pricing, thanks to collecting and analyzing power consumption data in real time. This feature is of great significance to improve the efficiency and reliability of power grid. However, in the process of power data acquisition, security threats need to be considered with respect to the leakage of user’s privacy. In addition, based on the principle of minimum necessary knowledge, the statistical information of various power consumption data should only be read by the designated authorized entity. To address the above problems, a privacy-preserving data aggregation with fine grained access control for smart grid was proposed. The scheme used Horner rule to aggregate multi-user and multi region power consumption data in a multi-dimensional way. The homomorphic encryption was used to ensure the privacy of user power consumption data, the digital signature was used to ensure authenticity of power consumption data. And the proxy re-encryption was used to achieve fine-grained access control of aggregated data, that is to say, the designated authorized entity could only read the aggregated data. Security analyses show that the proposed scheme can not only guarantee user’s privacy and the integrity of power consumption data, but also enables fine-grained access control of the aggregated data. Therefore, the scheme is suitable for real-world applications.

Key words: smart grid, privacy preservation, homomorphic encryption, batch verification, data aggregation

CLC Number: