(k, e)-anonymous for ordinal data

• Main Authors: Surapon Riyana, Nattapon Harnsamut, Torsak Soontornphand, Juggapong Natwichai
• 格式: Conference Proceeding
• 出版: 2018
• 主題: Computer Science
• 在線閱讀: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84964928536&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/54311

© 2015 IEEE. Currently, the data can be gathered, analyzed, and utilized easier than ever with the aiding of Big Data technologies such as mobile devices, elastic computing platform, or convenient software tools. Thus, privacy of such data could become a bigger issue as well. In this paper, we propose to extend the capability of a prominent privacy preservation model, (k, e)-anonymous to further provide a better option for privacy preservation. We propose to add a support for the privacy-sensitive ordinal data-type to such model, since it originally supports only numerical data. The experiments are conducted to show the characteristics of the modified model. From the results, we can conclude that the characteristics after our work has been applied are very similar to the original, and thus it can be effectively applied to the privacy problem.