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歡迎引用我們最近發(fā)表在IEEE Transactions on Industrial Informatics的文章
來(lái)源: 劉憶寧/
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2018-03-01


Yining Liu, Wei Guo, Chun-I Fan, Liang Chang, Chi Cheng. A Practical Privacy-Preserving Data Aggregation (3PDA) Scheme for Smart Grid, IEEE Transactions on Industrial InformaticsDOI: 10.1109/TII.2018.2809672


Abstract: The real-time electricity consumption data can be used in value-added service such as big data analysis, meanwhile the single user’s privacy needs to be protected. How to balance the data utility and the privacy preservation is a vital issue, where the privacy-preserving data aggregation could be a feasible solution. Most of the existing data aggregation schemes rely on a trusted third party (TTP). However, this assumption will have negative impact on reliability, because the system can be easily knocked down by the Denial of Service (DoS) attack. In this paper, a practical privacy-preserving data aggregation scheme is proposed without TTP, in which the users with some extent trust construct a virtual aggregation area to mask the single user’s data, and meanwhile, the aggregation result almost has no effect for the data utility in large scale applications. The computation cost and communication overhead are reduced  in order to promote the practicability. Moreover, the security analysis and the performance evaluation show that the proposed scheme is robust and efficient.


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