An approach for prevention of privacy breach and information leakage in sensitive data mining. (July 2015)

• Record Type: Journal Article
• Authors: Prakash, M.; Singaravel, G.
• Is Part Of: Computers & electrical engineering. Volume 45(2015)
• Page Start: 134
• Page End: 140
• Publication Date: 2015-07
• Subjects: Anonymization -- Data mining -- Privacy -- Privacy preserving -- Privacy preserving techniques -- Sensitive data publishing; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.01.016
• Languages: English
• ISSNs: 0045-7906

Abstract

Graphical abstract: Highlights: It prevents homogeneity, skewness, similarity and background knowledge attacks. The privacy is ensured while publishing sensitive data. Only fewer partitioning need to be done for a stronger privacy requirement. It gives better efficiency over the previous approaches. Abstract: Government agencies and many non-governmental organizations often need to publish sensitive data that contain information about individuals. The sensitive data or private data is an important source of information for the agencies like government and non-governmental organization for research and allocation of public funds, medical research and trend analysis. The important problem here is publishing data without revealing the sensitive information of individuals. This sensitive or private information of any individual is essential to several data repositories like medical data, census data, voter registration data, social network data and customer data. In this paper a personalized anonymization approach is proposed which preserves the privacy while the sensitive data is published. The main contributions of this paper are three folds: (i) the definition of the data collection and publication process, (ii) the privacy framework model and (iii) personalized anonymization approach. The experimental analysis is presented at the end; it shows this approach performs better over the distinct l -diversity measure, probabilistic l -diversity measure and k -anonymity with t -closeness measure.