Online Social Network (OSN) profiles help users to create first impressions on other users and therefore lead to various social benefits. However, users can become the victims of privacy harms such as identity theft, stalking or discrimination due to the personal data revealed in these profiles. So they have to carefully select the privacy settings for their profile attributes, keeping in mind this trade-off between privacy and social benefit. Since a profile consists of several attributes and users usually do not fully understand how the revelation of different attribute combinations can lead to privacy harms, this task is not easy. Without any support, privacy concerned users may take decisions that lead to sub-optimal social benefits or expose them to privacy risks or both. Therefore, in this paper, we develop a user-friendly model, based on Integer Programming (IP), to aid in this decision process. More precisely, our model provides an OSN user with easy-to-implement suggestions about the privacy settings of his profile attributes such that he can achieve the maximum social benefit while protecting himself from all or at least some major privacy risks. We propose methods to evaluate the privacy risks based on harm trees and the social benefits based on existing studies on the benefits of data sharing in OSNs. They form the founding pillars of our model.