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A Tool Prototype for Privacy Preserving Distributed Data Mining and Analytics
Abstract
Big Data Analytics techniques are becoming increasingly important in various scientific and commercial domains for intelligence gathering and decision making. Data Privacy is one of the key challenges that hinders in the intended implementation of these techniques. In several government and private organizations, data stores are located at different sites and bringing data together in centralized location for analysis is not possible due to privacy concerns. Hence, there is a strong need for data mining tool to solve the problems which involve creation of privacy preserved comprehensive view for data stored across standalone repositories or silos. There are several Privacy Preserving Distributed Data Mining algorithms and techniques available in literature. However they are not readily available in the form of tools or libraries for usage. In this paper we would present the design and implementation of a tool prototype to perform Privacy Preserving Data Mining and Analytics in distributed environment. The benefit and usefulness of the tool prototype is demonstrated for Census Data case study. We strongly believe that the complete implementation of this tool would result in effective usage, efficient development and evaluation of various Privacy Preserving Distributed Data Mining techniques which in turn could address Big Data privacy challenge.
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References
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