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.
In REF , the authors presented an access control mechanism to protect textual contents in online social networks which is enforced to be transparent to content publishers and readers.
Privacy-driven Access Control in Social Networks by Means of Automatic Semantic Annotation
Computer Communications 76: 12-25 (2016)
Computer Science