Privacy Preserving of Two Collaborating Parties Using Fuzzy C-Means Clustering

Abstract

In this smart and rapidly growing computing world, most of the organizations and companies needs to share necessary information (data) to third parties for their analysis which plays major role in decision making. Any data generally consists of sensitive (personal) information about people and organizations and when the sensitive information to be revealed to third parties, there is always chance of privacy loss. In this regard any organization or an individual requires the application of privacy preserving techniques to ensure data privacy, that means the data to be exchanged between two or more sites, without data loss or privacy violation. When data to be shared between multiple organizations or sites (parties) the privacy issues will be arises more. In this line we choose collaborative fuzzy clustering that gives better solutions to preserve privacy of data while sharing information between multiple parties to achieve combined results. Collaborative clustering is a concept or mechanism to find a common data points along with relationships within data residing at various individual data sites. We propose two ways of collaborative clustering using Fuzzy C-Means Clustering approach, one is Horizontal-PPFCM and other is Vertical-PPFCM. In both proposed methods the information is securely shared between two parties to compute the combined results and privacy also preserved. In fuzzy collaborative clustering two parties generate clusters with mutual collaboration of information they own at each individual sites and a collaborative objective function does all the necessary computations with granular information collected from each party. The overall process flow requires only the secured information and a common objective function for final outcomes. The proposed methods are working well for both horizontal and vertical collaborations and showing better results with assured data privacy.

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