Privacy Risk Assessment of Textual Publications in Social Networks

Recent studies have warned that, in Social Networks, users usually publish sensitive data that can be exploited by dishonest parties. Some mechanisms to preserve the privacy of the users of social networks have been proposed (i.e. controlling who can access to a certain published data); however, a still unsolved problem is the lack of proposals that enable the users to be aware of the sensitivity of the contents they publish. This situation is especially true in the case of unstructured textual publications (i.e., wall posts, tweets, etc.). These elements are considered to be particularly dangerous from the privacy point of view due to their dynamism and high informativeness. To tackle this problem, in this paper we present an automatic method to assess the sensitivity of the user’s textual publications according to her privacy requirements towards the other users in the social network. In this manner, users can have a clear picture of the privacy risks inherent to their publications and can take the appropriate countermeasures to mitigate them. The feasibility of the method is studied in a highly sensitive social network: PatientsLikeMe.