Privacy and security are major concerns for many users of social media. When users share information (e.g., data and photos) with friends, they can make their friends vulnerable to security and privacy breaches with dire consequences. With the continuous expansion of a user's social network, privacy settings alone are often inadequate to protect user's profile. In this research, we aim to address some critical issues related to privacy protection: (1) How can we measure and assess individual

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... r's vulnerability ? (2) With the diversity of one's social network friends, how can one figure out an effective approach to maintaining balance between vulnerability and social utility? In this work, first we present a novel way to define vulnerable friends from an individual user's perspective. User vulnerability is dependent on whether or not the user's friends' privacy settings protect the friend and the individual's network of friends (which includes the user). We show that it is feasible to measure and assess user vulnerability, and reduce one's vulnerability without changing the structure of a social networking site. The approach is to unfriend one's most vulnerable friends. However, when such a vulnerable friend is also socially important, unfriending him would significantly reduce one's own social status. We formulate this novel problem as vulnerability minimization with social utility constraints. We formally define the optimization problem, and provide an approximation algorithm with a proven bound. Finally, we conduct a large-scale evaluation of new framework using a Facebook dataset. We resort to experiments and observe how much vulnerability an individual user can decrease by unfriending a vulnerable friend. We compare performance of different unfriending strategies and discuss the security risk of new friend request. Additionally, by employing different forms of social utility, we confirm that balance between user vulnerability and social utility can be practically achieved.