Towards an ILP Approach for Learning Privacy Heuristics from Users' Regrets

Abstract

Disclosing private information in Social Network Sites (SNSs) often results in unwanted incidents for the users (such as bad image, identity theft, or unjustified discrimination), along with a feeling of regret and repentance. Regrettable online self-disclosure experiences can be seen as sources of privacy heuristics (best practices) that can help shaping better privacy awareness mechanisms. Considering deleted posts as an explicit manifestation of users’ regrets, we propose an Inductive Logic Programming (ILP) approach for learning privacy heuristics. In this paper we introduce the motivating scenario and the theoretical foundations of this approach, and we provide an initial assessment towards its implementation

Publication
Alhajj R., Hoppe H., Hecking T., Bródka P., Kazienko P. (eds) Network Intelligence Meets User Centered Social Media Networks. ENIC 2017. Lecture Notes in Social Networks, pp. 187–197, Springer, Cham. https://doi.org/10.1007/978-3-319-90312-5_13

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