Abstract
In recent years, the development of smart grid leads to the explosive growth of power big data. Power companies can analyze these data to provide personalized services to users. However, the analysis of power big data can have the risk of user privacy disclosure. The performance of the traditional algorithms is not satisfied due to the complexity of the power big data on preventing information leakage. Distributed and heterogeneous data generated in the operation, maintenance and other processes of electricity smart grid can cause the complexity of the data. This paper proposes a method of differential privacy to preserve privacy in the power big data publishing. The experimental results shows that the performance of our method is convincing.
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Acknowledgement
This work is supported by National Science Foundation of China Grant #61672088, Fundamental Research Funds for the Central Universities #2016JBM022 and #2015ZBJ007, Open Research Funds of Guangdong Key Laboratory of Popular High Performance Computers.
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Kong, P., Wang, X., Zhang, B., Li, Y. (2017). Differential Privacy in Power Big Data Publishing. In: Chen, G., Shen, H., Chen, M. (eds) Parallel Architecture, Algorithm and Programming. PAAP 2017. Communications in Computer and Information Science, vol 729. Springer, Singapore. https://doi.org/10.1007/978-981-10-6442-5_45
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DOI: https://doi.org/10.1007/978-981-10-6442-5_45
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