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