Privacy Preserving Data Mining using Cluster based Randomized Perturbation

Privacy is an important issue when one wants to make use of data that involves individual’s sensitive information. Research on protecting the privacy of individuals and the confidentiality of data has received contributions from many fields, including computer science, statistics, economics and social science. This is an area that attempts to answer the problem of how an organization, such as a hospital, government agency or insurance company, can release data to the public without violating the confidentiality of personal information. The technique used in this paper mainly focuses on Randomization for perturbation of values. The Randomization method is a technique for privacy preserving data mining in which noise is added to the data in order to mask the attribute values of records. The noise added is sufficiently large so that individual record values cannot be recovered. Therefore, techniques are designed to derive aggregate distributions from the perturbed records. Subsequently, data mining techniques can be developed in order to work with these aggregate distributions.