Research Article | OPEN ACCESS
Privacy Preserving Multiview Point Based BAT Clustering Algorithm and Graph Kernel Method for Data Disambiguation on Horizontally Partitioned Data
1J. Anitha and 2R. Rangarajan
1Department of IT, Sri Ramakrishna Engineering College, Vattamalaipalayam
2Department of ECE, RVS College of Engineering and Technology, Coimbatore,
Tamil Nadu 641022, India
Research Journal of Applied Sciences, Engineering and Technology 2015 6:640-651
Received: December 29, 2014 | Accepted: January 27, 2015 | Published: June 20, 2015
Abstract
Data mining has been a popular research area for more than a decade due to its vast spectrum of applications. However, the popularity and wide availability of data mining tools also raised concerns about the privacy of individuals. Thus, the burden of data privacy protection falls on the shoulder of the data holder and data disambiguation problem occurs in the data matrix, anonymized data becomes less secure. All of the existing privacy preservation clustering methods performs clustering based on single point of view, which is the origin, while the latter utilizes many different viewpoints, which are objects assumed to not be in the same cluster with the two objects being measured. To solve this all of above mentioned problems, this study presents a multiview point based clustering methods for anonymized data. Before that data disambiguation problem is solved by using Ramon-Gartner Subtree Graph Kernel (RGSGK), where the weight values are assigned and kernel value is determined for disambiguated data. Obtain privacy by anonymization, where the data is encrypted with secure key is obtained by the Ring-Based Fully Homomorphic Encryption (RBFHE). In order to group the anonymize data, in this study BAT clustering method is proposed based on multiview point based similarity measurement and the proposed method is called as MVBAT. However in this paper initially distance matrix is calculated and using which similarity matrix and dissimilarity matrix is formed. The experimental result of the proposed MVBAT Clustering algorithm is compared with conventional methods in terms of the F-Measure, running time, privacy loss and utility loss. RBFHE encryption results is also compared with existing methods in terms of the communication cost for UCI machine learning datasets such as adult dataset and house dataset.
Keywords:
BAT algorithm, cluster analysis, data disambiguation, data mining, distributed multi view point based clustering, graph partitioning, horizontal partitioning data, privacy , Ramon-Gartner Subtree Graph Kernel (RGSGK), Ring-Based Fully Homomorphic Encryption (RBFHE), security,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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