Abstract
Nowadays, the concern about privacy in online social networks has increased. However, the definition of an appropriate privacy policy might be a complex task, especially when several users are involved and have different privacy preferences. This problem usually appears when a user publishes a photo. In this paper, we propose a tool to automatically define the audience of a photo based on a trust metric. This metric uses a set of features (i.e., distance between users, number of people, emotions, etc.) obtained by the image analysis provided by IBM Cloud Visual Recognition Service. In a preliminary experiment considering 40 photos of 4 users, the results show that the proposed trust metric approximates the real trust relationships between users. We plan to integrate the tool into a real online social network.
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Acknowledgements
This work is partially supported by the Spanish Government project TIN2017-89156-R, by the FPI grants BES-2015-074498 and ACIF/2017/085, and the Post-Doc scholarship with the Ref. SP20170057.
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Taverner, J., Ruiz, R., del Val, E., Diez, C., Alemany, J. (2020). Image Analysis for Privacy Assessment in Social Networks. In: Omatu, S., Mohamad, M., Novais, P., Díaz-Plaza Sanz, E., García Coria, J. (eds) Distributed Computing and Artificial Intelligence, Special Sessions II, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 802. Springer, Cham. https://doi.org/10.1007/978-3-030-00524-5_1
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