An Efficient Hybrid Data Perturbation Approach to Preserve Privacy Kousika N.*, Dr. Premalatha K.** *Assistant Professor, Department of Computer Science and Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, India **Professor, Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India Online published on 14 October, 2016. Abstract Sharing of electronic data among organizations may disclose individual's sensitive information. Privacy preserving data mining (PPDM) plays a vital role in sensitive data protection. We propose an efficient hybrid data perturbation approach to address the privacy of both numeric and categorical attributes in electronic records. The categorical data are converted to numeric then original data are perturbed using S-based fuzzy membership function. Then attribute based multiplicative data perturbation is performed on different real world data sets to generate distorted data. Our proposed approach has proved its efficiency with K-means clustering and minimum distance classification of data before and after data transformation. Top Keywords Privacy Preserving, Data Perturbation, Fuzzy Logic, Multiplicative Data Perturbation, Classification, Clustering. Top |