Table of Contents Author Guidelines Submit a Manuscript
Journal of Electrical and Computer Engineering
Volume 2017, Article ID 2970673, 10 pages
https://doi.org/10.1155/2017/2970673
Research Article

The Anonymization Protection Algorithm Based on Fuzzy Clustering for the Ego of Data in the Internet of Things

1State Key Laboratory of Marine Resource Utilization in South China Sea, Haikou 570228, China
2College of Information Science & Technology, Hainan University, Haikou 570228, China
3College of Network, Haikou College of Economics, Haikou 571127, China

Correspondence should be addressed to Mengxing Huang; moc.361@90xmgnauh

Received 10 February 2017; Accepted 5 March 2017; Published 8 June 2017

Academic Editor: Jit S. Mandeep

Copyright © 2017 Mingshan Xie et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In order to enhance the enthusiasm of the data provider in the process of data interaction and improve the adequacy of data interaction, we put forward the concept of the ego of data and then analyzed the characteristics of the ego of data in the Internet of Things (IOT) in this paper. We implement two steps of data clustering for the Internet of things; the first step is the spatial location of adjacent fuzzy clustering, and the second step is the sampling time fuzzy clustering. Equivalent classes can be obtained through the two steps. In this way we can make the data with layout characteristics to be classified into different equivalent classes, so that the specific location information of the data can be obscured, the layout characteristics of tags are eliminated, and ultimately anonymization protection would be achieved. The experimental results show that the proposed algorithm can greatly improve the efficiency of protection of the data in the interaction with others in the incompletely open manner, without reducing the quality of anonymization and enhancing the information loss. The anonymization data set generated by this method has better data availability, and this algorithm can effectively improve the security of data exchange.