Design of privacy preserving model based on clustering involved anonymization along with feature selection

Abstract

Healthcare services has become a hug transformation due to the number of disease emerging at presently. Accordingly, enormous amount of data is generated regarding on the patient data. Hence, preserving privacy of patient data is the major concern to meet privacy requirements and to prevent healthcare data from numerous privacy attacks. To protect the information from privacy attack, various studies were performed to safeguard the patient's data but, these methods have several issues when preserving privacy that increases proportionally as the data dimension increased in multiple approaches, which produces less data quality, more information loss and more execution time.Thus, selecting relevant attributes plays an important factor for improving the efficiency of any preserving algorithm. In order to overcome these concerns, in this paper proposed a privacy preserving model based on clustering involved anonymization along with feature selection approaches. The proposed model consist of two phases such as, feature selection and anonymization. In the initial phase, the relevant features are selected using Symmetrical Uncertainty (SU) and the redundant features present in the dataset are removed utilizing Spearman's Correlation Coefficient. In the second phase, the privacy preservation is performed using Utility Preserved Anonymization (UPA) algorithm. Also, the proposed algorithm reduces the dimensionality of data to ease the process of forming clusters for anonymization. The experimental analysis using real time datasets to verifies the effectiveness of the proposed method. The results show high sensitivity (up to 98.63%) and high accuracy (up to 98%), allowing us to claim efficient attribute selection for anonymization. Hence proved the proposed method reduces clustering's complexity by removing the irrelevant attributes effectively.