An optimizing and differentially private clustering algorithm for mixed data in SDN-based smart grid

Zefang Lv, Lirong Wang, Zhitao Guan*, Jun Wu, Xiaojiang Du, Hongtao Zhao, Mohsen Guizani

*この研究の対応する著者

研究成果: Article査読

23 被引用数 (Scopus)

抄録

Software-defined network (SDN) is widely used in smart grid for monitoring and managing the communication network. Big data analytics for SDN-based smart grid has got increasing attention. It is a promising approach to use machine learning technologies to analyze a large amount of data generated in SDN-based smart grid. However, the disclosure of personal privacy information must receive considerable attention. For instance, data clustering in user electricity behavior analysis may lead to the disclosure of personal privacy information. In this paper, an optimizing and differentially private clustering algorithm named ODPCA is proposed. In the ODPCA, the differentially private K-means algorithm and K-modes algorithm are combined to cluster mixed data in a privacy-preserving manner. The allocation of privacy budgets is optimized to improve the accuracy of clustering results. Specifically, the loss function that considers both the numerical and categorical attributes between true centroids and noisy centroids is analyzed to optimize the allocation the privacy budget; the number of iterations of clustering is set to a fixed value based on the total privacy budget and the minimal privacy budget allocated to each iteration. It is proved that the ODPCA can meet the differential privacy requirements and has better performance by comparing with other popular algorithms.

本文言語English
論文番号8681031
ページ(範囲)45773-45782
ページ数10
ジャーナルIEEE Access
7
DOI
出版ステータスPublished - 2019
外部発表はい

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • 材料科学(全般)
  • 工学(全般)

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