Affiliation:
1. School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China
2. School of Information Engineering, East University of Heilongjiang, Harbin 150086, China
Abstract
The evolution of smart grids improves the sustainability, controllability, stability, and efficiency of traditional power grids. There is a challenging issue in smart grids with protecting users’ privacy while collecting and controlling individual fine-grained data. To ensure data integrity and address the privacy issue, differential privacy protection is an efficient method to resist differential attacks on aggregated data. However, due to differential noise and faulty smart meters, the problem of differential noise deviation has a great impact on the utility of aggregated data. In this paper, we further supplement the previous work by improving the prediction method, forming a relatively complete DP protection scheme (DPP-UFT) with fault tolerance, and providing a detailed performance evaluation process. The experimental results show that the proposed method of adding differential noise based on the estimated failure rate is related to the estimated failure rate and the noise factor. Compared with several other related literature, it has achieved a higher data utilization effect.
Funder
the Natural Science Foundation of Heilongjiang Province of China
the research start-up funds of Guangdong Polytechnic Normal University
Subject
Physics and Astronomy (miscellaneous),General Mathematics,Chemistry (miscellaneous),Computer Science (miscellaneous)
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