A New flustering approach for Privacy Preserving Data Fishing in Tele-Health Care Systems

Abstract

The genesis of the privacy preserving data mining techniques haul out the relevant intellect from mammoth amount of data in Tele-health care systems, while shielding at the same time sensitive information. A number of data fishing techniques, integrating privacy protection mechanisms, have been developed that allow one to smokescreen sensitive item sets or patterns, ahead of the execution of the data fishing process. An imperative issue is to settle on which ones among these privacy preserving techniques are superior enough to protect sensitive information. In this paper, we analyse the existing privacy breaches data perturbation techniques. With the impetus of increasing the data utility, and without compromising the simplicity of the process of perturbation, we have proposed a new variant of the random projection-based perturbation technique for mystifying the tele-health care data of individuals called the flustering approach. We have implemented and evaluated the competence of flustering technique on our own conceptual framework. We hope the proposed solution will get hold of new techniques, paving the way for a research track and working well, according to the evaluation metrics, including hiding effects, data utility and time performance.