Community detection for access-control decisions: Analysing the role of homophily and information diffusion in Online Social Networks

Access-Control Lists (ACLs) (a.k.a. friend lists) are one of the most important privacy features Online Social Networks (OSNs) as they allow users to restrict audience their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs since it normally requires assessing trustworthiness large number contacts. In principle, community detection algorithms be leveraged support generation by mapping set examples (i.e. contacts labelled untrusted) emerging communities inside user's ego-network. However, unlike users' access-control preferences, traditional community-detection do not take homophily characteristics such into account attributes shared among members). Consequently, this strategy may lead inaccurate ACL configurations breaches under certain scenarios. This work investigates use for automatic in OSNs. Particularly, analyses performance aforementioned approach different conditions through simulation model. Furthermore, private information reach scope untrusted recipients re-sharing affordances OSNs, diffusion processes also modelled taken explicitly account. Altogether, removal gatekeeper nodes is further explored counteract unwanted data dissemination.