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المؤلفLv, Zefang
المؤلفWang, Lirong
المؤلفGuan, Zhitao
المؤلفWu, Jun
المؤلفDu, Xiaojiang
المؤلفZhao, Hongtao
المؤلفGuizani, Mohsen
تاريخ الإتاحة2022-11-10T09:47:21Z
تاريخ النشر2019
اسم المنشورIEEE Access
المصدرScopus
المصدر2-s2.0-85064544712
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/ACCESS.2019.2909048
معرّف المصادر الموحدhttp://hdl.handle.net/10576/36121
الملخصSoftware-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. 2013 IEEE.
راعي المشروعThis work was supported by the Beijing Natural Science Foundation under Grant 4182060.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعbig data
clustering
Differential privacy
machine learning
SDN-based smart grid
العنوانAn optimizing and differentially private clustering algorithm for mixed data in SDN-based smart grid
النوعArticle
الصفحات45773-45782
رقم المجلد7
dc.accessType Open Access


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