Privacy preserving technique in data mining by using Chinease remainder theorem

Abstract

Human users are facing lots of problems, when they are transmitting sensitive data and confidential data. The sensitive data is intended to share between only authorized persons, not for all. The collection and sharing of person specific sensitive data for data mining raise serious concerns about the privacy of individuals. Privacy preserving data mining also concentrate on sensitive knowledge pattern that can be exposed when mining the data. Therefore, researchers, for a long time period, have been investigating paths to provide privacy for sensitive data in data mining analysis task process. So, many techniques have been introduced on privacy preserving issues in data mining by using equivalence operator. In this paper we proposed an effective and efficient approach based on Chinease remainder theorem (CRT) that uses congruence operator to provide privacy of sensitive data in the transformation of extracted data mining analysis from servers to clients. © 2012 Springer-Verlag.