International Journal of Scientific and Research Publications

IJSRP, Volume 5, Issue 5, May 2015 Edition [ISSN 2250-3153]


An Efficient K-Means Clustering by using Combination of Additive and Multiplicative Data Perturbation for Privacy Preserving Data Mining
      Bhupendra Kumar Pandya, Umesh kumar Singh, Keerti Dixit
Abstract: The collection of digital information by governments, corporations, and individuals has created tremendous opportunities for knowledge- and information-based decision making. Driven by mutual benefits, or by regulations that require certain data to be published, there is a demand for the exchange and publication of data among various parties. Data in its original form, however, typically contains sensitive information about individuals, and publishing such data will violate individual privacy. Privacy preserving data mining (PPDM) tends to transform original data, so that sensitive data are preserved. In this research paper we analysis CAMDP (Combination of Additive and Multiplicative Data Perturbation) technique for k-means clustering as a tool for privacy-preserving data mining. We can show that K-Means Clustering algorithm can be efficiently applied to the transformed data and produce exactly the same results as if applied to the original data.

Reference this Research Paper (copy & paste below code):

Bhupendra Kumar Pandya, Umesh kumar Singh, Keerti Dixit (2018); An Efficient K-Means Clustering by using Combination of Additive and Multiplicative Data Perturbation for Privacy Preserving Data Mining; Int J Sci Res Publ 5(5) (ISSN: 2250-3153). http://www.ijsrp.org/research-paper-0515.php?rp=P414056
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