A framework for ensemble classification and sensitivity analysis in privacy preserving data mining
P. Chandrakanth, M. S. Anbarasi · International Journal of Computational Systems Engineering · 2019
The perturbation mechanism for data streams is a challenging task. In the emerging world, data is erupting from various sources. The core applications need care on the data streams for further analysis and experimentation. As the micro data available with the core applications shall not be revealed to the public without taking any chance of breach, the perturbation challenges the analysis to get through the like results as of on the original datasets. In this paper, we have applied a concept of Perlin noise to distract the original data from the eyes of the analysts, however allowing them to perform their activities well. Applying security dynamically on such data is a challenging task. This paper deals about the concepts of generation of smooth noise and syntactic perturbation mechanism on the selective tuples as selective perturbation.