An analysis of Euclidean Distance preserving perturbation for Privacy Preserving Data Mining
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2014, Vol 2, Issue 10
Abstract
Privacy preserving data mining is a novel research direction in data mining. In recent years, with the rapid development in Internet, data storage and data processing technologies, privacy preserving data mining has been drawn increasing attention. Recently, distance preserving data perturbation has gained attention because it mitigates the privacy/accuracy trade-off by guaranteeing perfect accuracy. Many important data mining algorithms can be efficiently applied to the transformed data and produce exactly the same results as if applied to the original data. e.g., distance-based clustering and k-nearest neighbor classification].In this research paper we analysis Euclidean distance-preserving data perturbation as a tool for privacy-preserving data mining.
Authors and Affiliations
Bhupendra Kumar Pandya, Umesh Kumar Singh, Keerti Dixit
Keywords
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