By Topic

Wavelet-Based Data Perturbation for Simultaneous Privacy-Preserving and Statistics-Preserving

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

3 Author(s)
Lian Liu ; Dept. of Comput. Sci., Univ. of Kentucky, Lexington, KY ; Jie Wang ; Jun Zhang

With the rapid development of data mining technologies, preserving privacy in certain data becomes a challenge to data mining applications in many fields, especially in medical, financial and homeland security fields. We present a privacy-preserving strategy based on wavelet perturbation to keep the data privacy and data statistical properties and data mining utilities at the same time. Our mathematical analyses and experimental results show that this method can keep the distance before and after perturbation and it can preserve the basic statistical properties of the original data while maximizing the data utilities. Through experiments on real-life datasets, we conclude that this method is a promising privacy-preserving and statistics-preserving technique.

Published in:

2008 IEEE International Conference on Data Mining Workshops

Date of Conference:

15-19 Dec. 2008