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AuthorGuan, Zhitao
AuthorLv, Zefang
AuthorSun, Xianwen
AuthorWu, Longfei
AuthorWu, Jun
AuthorDu, Xiaojiang
AuthorGuizani, Mohsen
Available date2022-11-27T08:38:04Z
Publication Date2020-10-01
Publication NameIEEE Transactions on Network Science and Engineering
Identifierhttp://dx.doi.org/10.1109/TNSE.2020.2985096
CitationGuan, Z., Lv, Z., Sun, X., Wu, L., Wu, J., Du, X., & Guizani, M. (2020). A differentially private big data nonparametric Bayesian clustering algorithm in smart grid. IEEE Transactions on Network Science and Engineering, 7(4), 2631-2641.‏
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85083458874&origin=inward
URIhttp://hdl.handle.net/10576/36721
AbstractSmart systems, including smart grid (SG) and Internet of Things (IoT), have been playing a critical role in addressing contemporary issues. Taking full advantage of the big data generated by the smart grid can enhance the system stability and reliability, increase asset utilization, and offer better customer experience. To better support the data-driven smart grid, the machine learning technologies such as cluster analysis can be applied to process the massive data generated in smart grid. However, the process of cluster analysis may cause the disclosure of personal private information. In this paper, to achieve privacy-preserving cluster analysis in smart grid, we propose IDPC, a Differentially Private Clustering algorithm based on the Infinite Gaussian mixture model (IGMM). IDPC uses a combination of nonparametric Bayesian method and differential privacy. The nonparametric Bayesian method allows certain parameters to change along with the data and it is usually adopted in a clustering algorithm without a fixed number of clusters. The Laplace mechanism is used in data releasing process to make IDPC differentially private. We present how to make the nonparametric Bayesian clustering algorithm differentially private by adding Laplace noise. By security analysis and performance evaluation, IDPC is proved to be privacy-preserving as well as efficient.
SponsorThe work was supported in part by Beijing Natural Science Foundation under Grant 4182060 and in part by the National Natural Science Foundation of China under Grant 61972148.
Languageen
PublisherIEEE Computer Society
SubjectBig data
Clustering
Differential privacy
Nonparametric Bayesian Method
Smart grid
TitleA Differentially Private Big Data Nonparametric Bayesian Clustering Algorithm in Smart Grid
TypeArticle
Pagination2631-2641
Issue Number4
Volume Number7
dc.accessType Abstract Only


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