Abstract
Recent trends in ubiquitous computing target to provide user-controlled servers, providing a single point of access for managing different personal data in different Online Social Networks (OSNs), i.e. profile data and resources from various social interaction services (e.g., LinkedIn, Facebook, etc.). Ideally, personal data should remain independent of the environment, e.g., in order to support flexible migration to new landscapes. Such information interoperability can be achieved by ontology-based information representation and management. In this paper we present achievements and experiences of the di.me project, with respect to access control and privacy preservation in such systems. Special focus is put on privacy issues related to linkability and unwanted information disclosure. These issues could arise for instance when collecting and integrating information of different social contacts and their live streams (e.g., activity status, live posts, etc.). Our approach provides privacy recommendations by leveraging (1) the detection of semantic equivalence between contacts as portrayed in online profiles and (2) NLP techniques for analysing shared live streams. The final results after 3 years are presented and the portability to other environments is shortly discussed.
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Notes
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As in cases of many alpha releases it is almost impossible to get reliable results for an evaluation, we restricted the set of systems for this analysis to the following five stable system implementations: Diaspora, Friendica, Jappix, Kune and StatusNet.
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The project implemented a user-driven design process and requirements were refined several studies and validations.
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Cf. http://www.dime-project.eu for details on the consortium application partners.
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Also in terms of innovation!.
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Although an authoritative scientific evaluation is still forthcoming, preliminary experiments suggest the following indicative F-measures: Persons: 51 %, Locations: 60 %, Events: 45 %.
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Heupel, M., Fischer, L., Bourimi, M., Scerri, S. (2015). Ontology-Enabled Access Control and Privacy Recommendations. In: Atzmueller, M., Chin, A., Scholz, C., Trattner, C. (eds) Mining, Modeling, and Recommending 'Things' in Social Media. MUSE MSM 2013 2013. Lecture Notes in Computer Science(), vol 8940. Springer, Cham. https://doi.org/10.1007/978-3-319-14723-9_3
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