Achieving Data Privacy in a Distributed Environment Using Geometrical Transformation
G. Manikandan, Nivedita Sairam, S. Jayashree · 2013
Advances in data acquirement methods have resulted in gathering and storing cosmic quantities of data. For communal profit, these data are shared among various organizations for investigative purposes. In various astonishing circumstances this data sharing may reveal some concealed information thus raising privacy concerns. This paper introduces a proficient privacy-preserving technique for a distributed environment. Prior to data sharing at each site a geometrical transformation is used to modify the original data which is followed by a normalization process. To verify the untried results k-means clustering algorithm is used and it is apparent from our experimental outcome that our approach preserves privacy and also ensures accuracy in a distributed environment.