Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

All liaisons are dangerous when all your friends are known to us

All liaisons are dangerous when all your friends are known to us All Liaisons are Dangerous When All Your Friends are Known to Us Daniel Gayo-Avello University of Oviedo Despacho 57, planta baja, Edi cio de Ciencias C/Calvo Sotelo s/n 33007 Oviedo (SPAIN) dani@uniovi.es ABSTRACT Online Social Networks (OSNs) are used by millions of users worldwide. Academically speaking, there is little doubt about the usefulness of demographic studies conducted on OSNs and, hence, methods to label unknown users from small labeled samples are very useful. However, from the general public point of view, this can be a serious privacy concern. Thus, both topics are tackled in this paper: First, a new algorithm to perform user pro ling in social networks is described, and its performance is reported and discussed. Secondly, the experiments “conducted on information usually considered sensitive “ reveal that by just publicizing one ™s contacts privacy is at risk and, thus, measures to minimize privacy leaks due to social graph data mining are outlined. In that context, labeling algorithms can exploit a property of social networks: the tendency of people to relate more likely with those sharing similar traits, or homophily. This phenomenon is pervasive to very di €erent social networks, and it has been revealed that a http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

All liaisons are dangerous when all your friends are known to us

Association for Computing Machinery — Jun 6, 2011

Loading next page...
/lp/association-for-computing-machinery/all-liaisons-are-dangerous-when-all-your-friends-are-known-to-us-ji3Qvz6g2y

References (19)

Datasource
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISBN
978-1-4503-0256-2
doi
10.1145/1995966.1995991
Publisher site
See Article on Publisher Site

Abstract

All Liaisons are Dangerous When All Your Friends are Known to Us Daniel Gayo-Avello University of Oviedo Despacho 57, planta baja, Edi cio de Ciencias C/Calvo Sotelo s/n 33007 Oviedo (SPAIN) dani@uniovi.es ABSTRACT Online Social Networks (OSNs) are used by millions of users worldwide. Academically speaking, there is little doubt about the usefulness of demographic studies conducted on OSNs and, hence, methods to label unknown users from small labeled samples are very useful. However, from the general public point of view, this can be a serious privacy concern. Thus, both topics are tackled in this paper: First, a new algorithm to perform user pro ling in social networks is described, and its performance is reported and discussed. Secondly, the experiments “conducted on information usually considered sensitive “ reveal that by just publicizing one ™s contacts privacy is at risk and, thus, measures to minimize privacy leaks due to social graph data mining are outlined. In that context, labeling algorithms can exploit a property of social networks: the tendency of people to relate more likely with those sharing similar traits, or homophily. This phenomenon is pervasive to very di €erent social networks, and it has been revealed that a

There are no references for this article.