Title: Haphazard, enhanced haphazard and personalised anonymisation for privacy preserving data mining on sensitive data sources
Authors: M. Prakash; G. Singaravel
Addresses: Department of Computer Science and Engineering, K.S.R. College of Engineering Tiruchengode, Tamilnadu, India ' Department of Computer Science and Engineering, K.S.R. College of Engineering Tiruchengode, Tamilnadu, India
Abstract: Privacy preserving data mining is a fast growing new era of research due to recent advancements in information, data mining, communications and security technologies. Government agencies and many other non-governmental organisations often need to publish sensitive data that contain information about individuals. The important problem is publishing data about individuals without revealing sensitive information about them. A breach in the security of a sensitive data may expose the private information of an individual, or the interception of a private communication may compromise the security of a sensitive data. The objective of the research is to publish data without revealing the sensitive information of individuals, at the same time the miner need to discover non-sensitive knowledge. To achieve the above objective, haphazard anonymisation, enhanced haphazard anonymisation and personalised anonymisation are proposed for privacy and utility preservation. The performances are evaluated based on vulnerability to attacks, efficiency and data utility.
Keywords: analytics; anonymisation; big data; data mining; data publishing; microdata; privacy preserving; privacy; sensitive data.
International Journal of Business Intelligence and Data Mining, 2018 Vol.13 No.4, pp.456 - 474
Available online: 17 Jan 2018 *Full-text access for editors Access for subscribers Purchase this article Comment on this article