Improved Generalization for Secure Personal Data Publishing Using Deviation

Abstract

The human being leads to an improved lifestyle because of better healthcare facilities, which is possible by practicing 'sharing.' Sharing of minimal, necessary Electronic Health Records (EHRs) with legitimate grounds have made it easier for researchers and scientists to apply statistics, develop medicines, and improve healthcare facilities. However, it invites many security and privacy concerns. The data may contain personally identifiable attributes; publishing such data will cause problems. Several studies have been proposed to maintain a balance between privacy and utility, but they were not up-to-the-mark. We propose an improved technique for anonymizing EHR, quasi-identifiers to retain data privacy, and maintains utility while keeping it anonymous. The proposed technique performs anonymization by assigning data into classes, adding uncertainty to it, based on the deviation. Based on experiments, it is concluded that the proposed scheme performs better in terms of privacy, utility and is better to its predecessor while making it difficult to trace back.