Cart (Loading....) | Create Account
Close category search window
 

Privacy Preserving Sequential Pattern Mining Based on Data Perturbation

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
$31 $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

3 Author(s)
Wei-Min Ouyang ; Shanghai Univ. of Sport, Shanghai ; Hong-Liang Xin ; Qin-Hua Huang

Data mining is to discover previously unknown, potentially useful and nontrivial knowledge, patterns or rules. Because databases may have some sensitive information which should not be leaked out, it is nontrivial to study data mining techniques without neglecting sensitive information, i.e., privacy-preserving data mining. In this paper, a new technique has been proposed for privacy-preserving mining of sequential patterns based on data perturbation. Experimental results show that the reconstructing support of frequent sequences can achieve a rather high level of accuracy.

Published in:

Machine Learning and Cybernetics, 2007 International Conference on  (Volume:6 )

Date of Conference:

19-22 Aug. 2007

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.