  In online social networks (OSN), users quite usually disclose sensitive
information about themselves by publishing messages. At the same time, they are
(in many cases) unable to properly manage the access to this sensitive
information due to the following issues: i) the rigidness of the access control
mechanism implemented by the OSN, and ii) many users lack of technical
knowledge about data privacy and access control. To tackle these limitations,
in this paper, we propose a dynamic, transparent and privacy-driven access
control mechanism for textual messages published in OSNs. The notion of
privacy-driven is achieved by analyzing the semantics of the messages to be
published and, according to that, assessing the degree of sensitiveness of
their contents. For this purpose, the proposed system relies on an automatic
semantic annotation mechanism that, by using knowledge bases and linguistic
tools, is able to associate a meaning to the information to be published. By
means of this annotation, our mechanism automatically detects the information
that is sensitive according to the privacy requirements of the publisher of
data, with regard to the type of reader that may access such data. Finally, our
access control mechanism automatically creates sanitized versions of the users'
publications according to the type of reader that accesses them. As a result,
our proposal, which can be integrated in already existing social networks,
provides an automatic, seamless and content-driven protection of user
publications, which are coherent with her privacy requirements and the type of
readers that access them. Complementary to the system design, we also discuss
the feasibility of the system by illustrating it through a real example and
evaluate its accuracy and effectiveness over standard approaches.
