When people expose their private life in online social networks, this doesn't mean that they do not care about their privacy, but they do lack tools to evaluate the risks and to protect their data. To address this issue, we have previously designed the FORPS system (Friends Oriented Reputation Privacy Score) that evaluates the dangerousness of people who suggest to become our friends, by computing their propensity to propagate sensitive information. In this paper, we introduce a multi-agent simulation model that allows evaluating the long-term and large scale effects of our system based on high number of interactions between simulated users. We show that in comparison with a simple decision process, different variants of the FORPS system produce better results in terms of estimation of the requestor's dangerousness, the convergence speed and the resistance to rumor phenomena.