TY - JOUR
T1 - A Differentially Private Big Data Nonparametric Bayesian Clustering Algorithm in Smart Grid
AU - Guan, Zhitao
AU - Lv, Zefang
AU - Sun, Xianwen
AU - Wu, Longfei
AU - Wu, Jun
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - 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.
AB - 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.
KW - Big data
KW - Clustering
KW - Differential privacy
KW - Nonparametric Bayesian Method
KW - Smart grid
UR - http://www.scopus.com/inward/record.url?scp=85083458874&partnerID=8YFLogxK
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U2 - 10.1109/TNSE.2020.2985096
DO - 10.1109/TNSE.2020.2985096
M3 - Article
AN - SCOPUS:85083458874
VL - 7
SP - 2631
EP - 2641
JO - IEEE Transactions on Network Science and Engineering
JF - IEEE Transactions on Network Science and Engineering
IS - 4
M1 - 9057414
ER -