NDLI: A K-anonymity clustering algorithm based on the information entropy

Content Provider	IEEE Xplore Digital Library
Author	Jianpei Zhang Ying Zhao Yue Yang Jing Yang
Copyright Year	2014
Description	Author affiliation: Coll. of Comput. Sci. & Technol., Harbin Eng. Univ., Harbin, China (Jianpei Zhang; Ying Zhao; Yue Yang; Jing Yang)
Abstract	Data anonymization techniques are the main way to achieve privacy protection, and as a classical anonymity model, K-anonymity is the most effective and frequently-used. But the majority of K-anonymity algorithms can hardly balance the data quality and efficiency, and ignore the privacy of the data to improve the data quality. To solve the problems above, by introducing the concept of “diameter” and a new clustering criterion based on the parameter of the maximum threshold of equivalence classes, we proposed a K-anonymity clustering algorithm based on the information entropy. The results of experiments showed that both the algorithm efficiency and data security are improved, and meanwhile the total information loss is acceptable, so the proposed algorithm has some practicability in application.
Sponsorship	IEEE SMC Soc.
Starting Page	319
Ending Page	324
File Size	394735
Page Count	6
File Format	PDF
ISBN	9781479937769
DOI	10.1109/CSCWD.2014.6846862
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-21
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Entropy Clustering algorithms Algorithm design and analysis Data security Information entropy Loss measurement Classification algorithms K-anonymity clustering privacy preserving information entropy
Content Type	Text
Resource Type	Article

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