A Differentially Private Big Data Nonparametric Bayesian Clustering Algorithm in Smart Grid

Zhitao Guan*, Zefang Lv, Xianwen Sun, Longfei Wu, Jun Wu, Xiaojiang Du, Mohsen Guizani

*この研究の対応する著者

研究成果査読

5 被引用数 (Scopus)

抄録

Smart 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.

本文言語English
論文番号9057414
ページ(範囲)2631-2641
ページ数11
ジャーナルIEEE Transactions on Network Science and Engineering
7
4
DOI
出版ステータスPublished - 2020 10 1
外部発表はい

ASJC Scopus subject areas

  • 制御およびシステム工学
  • コンピュータ サイエンスの応用
  • コンピュータ ネットワークおよび通信

フィンガープリント

「A Differentially Private Big Data Nonparametric Bayesian Clustering Algorithm in Smart Grid」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル