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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 RamonGartner 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.

Privacy Preserving Multiview Point Based BAT Clustering Algorithm and Graph Kernel Method for Data Disambiguation on Horizontally Partitioned Data