Authors:
Ammar Abuelgasim
and
Anne Kayem
Affiliation:
University of Cape Town, South Africa
Keyword(s):
Privacy Policies, Recommendation Systems, Social Networks.
Abstract:
Most Online Social Networks (OSNs) implement privacy policies that enable users to protect their sensitive information against privacy violations. However, observations indicate that users find these privacy policies cumbersome and difficult to configure. Consequently, various approaches have been proposed to assist users with privacy policy configuration. These approaches are however, limited to either protecting only profile attributes, or only protecting user-generated content. This is problematic, because both profile attributes and user-generated content can contain sensitive information. Therefore, protecting one without the other, can still result in privacy violations. A further drawback of existing approaches is that most require considerable user input which is time consuming and inefficient in terms of privacy policy configuration. In order to address these problems, we propose an automated privacy policy recommender system. The system relies on the expertise of existing O
SN users, in addition to the target user's privacy policy history to provide him/her with personalized privacy policy suggestions for profile attributes, as well as user-generated content. Results from our prototype implementation indicate that the proposed recommender system provides accurate privacy policy suggestions, with minimum user input.
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