By Topic

Privacy preserving research for re-publication multiple sensitive attributes in data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Xiaolin Zhang ; Department of Information and Engineering, Inner Mongolia University of Science and Technology, Baotou, China ; Lifeng Zhang

Previous works about privacy preserving data publication have most focused on static dataset, which have no update and need “one-time” releases. Only a little of literature has considered the serial data publication on dynamic dataset, but none of them consider perfectly. They can not against various kind of background, or the utility for serial data publishing is low. Based on theoretical analysis, we develop a new generalization principle that effectively limits the risk of Multiple Sensitive Attributes privacy disclosure in re-publication. The results show that our algorithm has higher degree of privacy protection and lower hiding rate.

Published in:

Computer Science and Automation Engineering (CSAE), 2011 IEEE International Conference on  (Volume:3 )

Date of Conference:

10-12 June 2011