Privacy preserving in Data mining & publishing, plays a major role in today networked world. It is important to preserve the privacy of the vital information corresponding to a data set. This process can be achieved by k-anonymization solution for classification. Along with the privacy preserving using anonymization, yielding the optimized data sets is also of equal importance with a cost effective approach. In this paper Top-Down Refinement algorithm has been proposed which yields optimum results in a cost effective manner. Bayesian Classification has been proposed in this paper to predict class membership probabilities for a data tuple for which the associated class label is unknown.
Tirumala, L Mohana and Rao, S. Srinivasa
"Optimization through Bayesian Classification on the k-Anonymized Data,"
International Journal of Computer Science and Informatics: Vol. 1
, Article 4.
Available at: https://www.interscience.in/ijcsi/vol1/iss4/4