Using community structure to control information sharing in online social networks

Abstract

The dominant role of social networking in the web is turning human relations into conduits of information flow. This means that the way information spreads on the web is determined to a large extent by human decisions. Consequently, information security lies on the quality of the collective decisions made by the users. Recently, many access control schemes have been proposed to control unauthorized propagation of information in online social networks; however, there is still a need for mechanisms to evaluate the risk of information leakage within social networks. In this paper, we present a novel community-centric confidentiality control mechanism for information flow management on the web. We use a Monte Carlo based algorithm to determine the potential spread of a shared data object and to inform the user of the risk of information leakage associated with different sharing decisions she can make in a social network. By using the information provided by our algorithm, the user can curtail sharing decisions to reduce the risk of information leakage. Alternatively, our algorithm can provide input for a fully- or semi-automatic sharing decision maker that will determine the outcomes of sharing requests. Our scheme also provides a facility to reduce information flowing to a specific user (i.e., black listing a specific user). We used datasets from Facebook and Flickr to evaluate the performance of the proposed algorithms under different sharing conditions. The simulation results indicate that our algorithm can effectively control information sharing to reduce the risk of information leakage.