Affiliations: [a] Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu | [b] Department of Computer Science, Mahalashmi Women’s College of Arts and Science, Paruthipattu, Avadi, Chennai, Tamilnadu
Correspondence:
[*]
Corresponding author. G. Murugaboopathi, Associate Professor, Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Tamilnadu. E-mail: dr.g.murugaboopathi@gmail.com.
Abstract: Privacy preservation in data publishing is the major topic of research in the field of data security. Data publication in privacy preservation provides methodologies for publishing useful information; simultaneously the privacy of the sensitive data has to be preserved. This work can handle any number of sensitive attributes. The major security breaches are membership, identity and attribute disclosure. In this paper, a novel approach based on slicing that adheres to the principle of k-anonymity and l-diversity is introduced. The proposed work withstands all the privacy threats by the incorporation of k-means and cuckoo-search algorithm. The experimental results with respect to suppression ratio, execution time and information loss are satisfactory, when compared with the existing approaches.
