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

Social Network Signatures: A Framework for Re-identification in Networked Data and Experimental Results

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

2 Author(s)
Hill, S. ; Univ. of Pennsylvania, Philadelphia, PA, USA ; Nagle, A.

Data on large dynamic social networks, such as telecommunications networks and the Internet, are pervasive. However, few methods conducive to efficient large-scale analysis exist. In this paper, we focus on the task of re-identification. Re-identification in the context of dynamic networks is a matching problem that involves comparing the behavior of networked entities across two time periods. Prior research has reported success in the domains of e-mail alias detection, author attribution, and identifying fraudulent consumers in the telecommunications industry. In this work, we address the question of "why are we able to re-identify entities on real world dynamic networks?" Our contribution is two-fold. First, we address the challenge of scale with a framework for matching that does not require pairwise comparisons to ascertain the similarity scores between networked entities. Second, we show our method is robust against missing links but less tolerant to noise. Using our framework, we provide a performance estimate for re-identification on networks based solely on their degree distribution and dynamics. This work has significant implications for re-identification problems where scale is a challenge as well as for problems where false negatives (e.g., when fraudulent consumers are not labeled as fraudulent) cannot be observed.

Published in:

Computational Aspects of Social Networks, 2009. CASON '09. International Conference on

Date of Conference:

24-27 June 2009

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.