Enabling Users to Balance Social Benefit and Privacy in Online Social Networks

Abstract

Attributes such as interests, workplace and relationship status in an Online Social Network (OSN) profile introduce a user to other OSN users. They can contribute to building new friendships as well as reviving and enhancing existing ones. However, the personal data revealed by the user himself or by his vicinity, i.e., his OSN friends, can also make him vulnerable to many privacy harms such as identity theft, stalking or sexual predation. So users have to carefully select the privacy settings for their profile attributes by keeping in mind the trade-off between privacy and social benefit. In this paper, we propose a usercentric two-phase approach, based on Integer Programming, to choose the right privacy settings. Our model assists the user to understand which privacy harms he can avoid, after tolerating residual risks, given his desired social benefit requirements and suggests the privacy settings he should adopt to achieve the maximum social benefit. Thus, users’ choices are based on both privacy risks and benefits, a view supported by the EU General Data Protection Regulation (GDPR). We have tested our approach on user profiles with varying vicinities and social benefit requirements.