Abstract
Knowledge discovery on social network data can benefit general public, since these data contain latent social trends and valuable information. Recent research finds that preserving data privacy plays a vital role in knowledge discovery. Therefore, social network data need to be anonymized to preserve users’ identity before the data can be released for research purposes. In this paper, we model social network data as directed graphs with signed edge weights; formally define privacy, attack models for the anonymization problem. Based on our analysis, we develop a graph anonymization approach. The other main contribution is our graph clustering algorithm which can effectively group similar graph nodes into clusters with minimum cluster size constraints. Finally, we carry out a series of experiments to evaluate the effectiveness and utility of our approach on anonymizing social
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
