Often, users of Social Network Sites (SNSs) like Facebook or Twitter have issues when controlling the access to their content. Access-control predictive models are used to recommend access-control configurations which are aligned with the users’ individual privacy preferences. One basic strategy for the prediction of access-control configurations is to generate access-control lists out of the emerging communities inside the user’s ego-network. That is, in a community-based fashion. Homophily, which is the tendency of individuals to bond with others who hold similar characteristics, can influence the network structure of SNSs and bias the users’ privacy preferences. Consequently, it can also impact the quality of the configurations generated by access-control predictive models that follow a community-based approach. In this work, we use a simulation model to evaluate the effect of homophily when predicting access-control lists in SNSs. We generate networks with different levels of homophily and analyse thereby its impact on access-control recommendations.