Privacy Preserving Clustering-A Hybrid Approach

This paper presents a privacy preserving clustering technique using hybrid approach. The technique mainly exploits a combination of isometric transformations i.e. translation, rotation and reflection transformations along with a secure random function in order to provide secrecy of user-specified attributes without losing accuracy in results. The proposed method was tested and evaluated in terms of several synthetic as well as real-life data and the performance has been found satisfactory in comparison to its other counterparts.