Measuring Profile Distance in Online Social Networks

Document type: Conference Papers
Peer reviewed: Yes
Full text:
Author(s): Niklas Lavesson, Henric Johnson
Title: Measuring Profile Distance in Online Social Networks
Conference name: International Conference on Web Intelligence, Mining and Semantics
Year: 2011
ISBN: 978-1-4503-0148-0
Publisher: ACM
City: Sogndal
Organization: Blekinge Institute of Technology
Department: School of Computing (Sektionen för datavetenskap och kommunikation)
School of Computing S-371 79 Karlskrona
+46 455 38 50 00
Authors e-mail:,
Language: English
Abstract: Online Social Networks (OSNs) provide new ways for people to communicate with one another and to share content. OSNs have become quite popular among the general population but their rapid growth has raised concerns about privacy and security. Many predict that the OSNs of today provide a glimpse of the future Internet infrastructure. Whether or not that will be true is difficult to say but what is certain is that the privacy, integrity, and security issues and concerns need to be addressed now. In fact, the mainstream media have uncovered a rising number of potential and occurring problems, including: identity theft, unauthorized sharing of private information, malicious behavior of OSN services and applications, and so on. This paper addresses several important security and privacy issues by focusing on one of the core concepts of OSNs; the user profile, which both includes private and public information that the user shares to different parties and the customized security and privacy settings of the user. We present a method for comparing user profiles, by measuring the distance between the profiles in metric space, and for determining how well an OSN application conforms to user privacy settings. We report on a case study in which the proposed method is applied to Facebook to demonstrate the applicability of the method as well as to motivate its theoretical foundation.
Subject: Computer Science\Electronic security
Computer Science\Artificial Intelligence
Computer Science\General
Keywords: online social networks, security settings, automatic configuration