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Ontology-enabled access control and privacy recommendations

 
: Heupel, M.; Fischer, L.; Bourimi, M.; Scerri, S.

:

Atzmüller, M.:
Mining, modeling, and recommending 'things' in social media. 4th international workshops, MUSE 2013 : Prague, Czech Republic, September 23, 2013, and MSM 2013, Paris, France, May 1, 2013; Revised selected papers; MUSE 2013 was held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2013) and the MSM 2013 was held in conjunction with ACM Hypertext
Cham: Springer International Publishing, 2015 (Lecture Notes in Computer Science 8940)
ISBN: 978-3-319-14722-2 (Print)
ISBN: 978-3-319-14723-9 (Online)
S.35-54
International Workshop on Mining Ubiquious and Social Environments (MUSE) <4, 2013, Prague>
International Workshop on Modeling Social Media (MSM) <2013, Paris>
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <2013, Prague>
Englisch
Konferenzbeitrag
Fraunhofer IAIS ()

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.

: http://publica.fraunhofer.de/dokumente/N-352468.html