The rapid growth of social networks, primarily Facebook, has coincided with an increasing concern over personal privacy. This explains why more and more users personalize their Facebook privacy settings. As a matter of fact, the list of friends is often one of the profile sections kept private, meaning that this information is perceived as sensible. In this paper, we study the robustness of this privacy protection feature, showing that it can be broken even in the less advantageous conditions for the adversary. To do this, we exploit both the potential information extracted from user alter accounts in Twitter and a social network property, recently demonstrated for Twitter, called interest assortativity. The preliminary experimental results reported in this paper, give a first evidence of the effectiveness of our attack, which succeeds even in the most difficult case that is when the information about the victim are minimum.
A threat to friendship privacy in facebook / F. Buccafurri, G. Lax, S. Nicolazzo, A. Nocera (LECTURE NOTES IN COMPUTER SCIENCE). - In: Availability, Reliability, and Security in Information Systems / [a cura di] F. Buccafurri, A. Holzinger, P. Kieseberg, A.M. Tjoa, E. Weippl. - [s.l] : Springer Verlag, 2016. - ISBN 978-3-319-45506-8. - pp. 96-105 (( convegno IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016 and Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016 tenutosi a Salzburg nel 2016 [10.1007/978-3-319-45507-5_7].
A threat to friendship privacy in facebook
S. Nicolazzo;
2016
Abstract
The rapid growth of social networks, primarily Facebook, has coincided with an increasing concern over personal privacy. This explains why more and more users personalize their Facebook privacy settings. As a matter of fact, the list of friends is often one of the profile sections kept private, meaning that this information is perceived as sensible. In this paper, we study the robustness of this privacy protection feature, showing that it can be broken even in the less advantageous conditions for the adversary. To do this, we exploit both the potential information extracted from user alter accounts in Twitter and a social network property, recently demonstrated for Twitter, called interest assortativity. The preliminary experimental results reported in this paper, give a first evidence of the effectiveness of our attack, which succeeds even in the most difficult case that is when the information about the victim are minimum.File | Dimensione | Formato | |
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