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A Robust Privacy Preservation by Combination of Additive and Multiplicative Data Perturbation for Privacy Preserving Data Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 120 - Number 1 |
| Year of Publication: 2015 |
| Authors: Bhupendra Kumar Pandya, Umesh Kumar Singh, Keerti Dixit |
10.5120/21192-3850
|
Bhupendra Kumar Pandya, Umesh Kumar Singh, Keerti Dixit . A Robust Privacy Preservation by Combination of Additive and Multiplicative Data Perturbation for Privacy Preserving Data Mining. International Journal of Computer Applications. 120, 1 ( June 2015), 25-28. DOI=10.5120/21192-3850
Abstract
Most of our daily activities are now routinely recorded and analysed by a variety of governmental and commercial organizations for the purpose of security and business related applications. From telephone calls to credit card purchases, from internet surfing to medical prescription refills, we generate data with almost every action we take. These data sets need to be analyzed for identifying patterns which can be used to predict future behaviour. However, data owners may not be willing to share the real values of their data due to privacy reason. Hence, some amount of privacy preservation needs to be done on data before it is released. Privacy preserving data mining (PPDM) tends to transform original data, so that sensitive data are preserved. In this paper we have proposed a new method CAMDP (Combination of Additive and Multiplicative Data Perturbation) for privacy preserving in data mining.
References
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- B. Pandya,U. K. Singh and K. Dixit, "Effectiveness of Multiplicative Data Perturbation for Perturbation for Privacy Preserving Data Mining" International Journal of Advanced Research in Computer Science, Vol. 5, No. 6 pp 112-115,2014.
- B. Pandya,U. K. Singh and K. Dixit, "Performance of Multiplicative Data Perturbation for Perturbation for Privacy Preserving Data Mining" International Journal for Research in Applied Science and Engineering Technology, Vol. 2, Issue VII, 2014.
- B. Pandya,U. K. Singh and K. Dixit, "An Analysis of Euclidean Distance Presrving Perturbation for Privacy Preserving Data Mining" International Journal for Research in Applied Science and Engineering Technology, Vol. 2, Issue X, 2014.
- B. Pandya,U. K. Singh and K. Dixit, "Performance of Euclidean Distance Presrving Perturbation for K-Means Clustering" International Journal of Advanced Scientific and Technical Research, Vol. 5, Issue 4, pp 282-289, 2014.
- B. Pandya,U. K. Singh and K. Dixit, "Performance of Euclidean Distance Presrving Perturbation for K-Nearest Neighbour Classification" International Journal of Computer Application, Vol. 105, No. 2, pp 34-36, 2014.
- B. Pandya,U. K. Singh and K. Dixit, "A Study of Projection Based Multiplicative Data Perturbation for Privacy Preserving Data Mining" International Journal of Application or Innovation in Engineering and Management, Vol. 3, Issue 11, pp 180-182,2014.
- B. Pandya,U. K. Singh and K. Dixit, "An Analysis of Projection Based Multiplicative Data Perturbation for K-Means Clustering" International Journal of Computer Science and Information Technologies, Vol. 5, Issue. 6, pp 8067-8069, 2014.
- B. Pandya,U. K. Singh and K. Dixit, "An Evaluation of Projection Based Multiplicative Data Perturbation for K-Nearest Neighbour Classification" International Journal of Science and Research, Vol. 3, Issue. 12, pp 681-684, 2014.
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