By Topic

Preserving Privacy in Social Networks against Homogeneity Attack

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)
Rui Zhang ; Sch. of Comput. Sci. & Technol., Huazhong Univ. of Sci. & Technol., Wuhan, China ; Binbin Qu

Social networks have gained growing popularity in various application domains. World Wide Web has facilitated the application of information collection, dissemination and analyses to a great extent. Privacy protection is therefore especially challenged in publishing network data, because an individual's network contents can be used for identifying themselves even if other identified information is removed. However, up to now, most of the work is paying close attention to structural attacks and to the best of our knowledge, there is no effort on how to resist homogeneity attack simultaneously. In this paper, we propose a model (called k-1 generalized graph) to protect against structural and homogeneity attacks and to develop an algorithm that produces k-1 generalized graph.

Published in:

Internet Technology and Applications, 2010 International Conference on

Date of Conference:

20-22 Aug. 2010