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Fuzzy logic decision based collaborative privacy management framework for online social networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Online Social Networks (OSNs) have become one of the most popular implement for interacting with people all over the world and sharing data with them. These data sometimes may be a co-owned data which involves multiple users, sharing co-owned data can cause privacy violation if co-owners are not happy with the owner's sharing privacy settings. To tackle privacy issues on co-owned data, collaborative privacy management has become a popular research area in recent years. In this work, we provide a fuzzy logic decision based collaborative privacy management framework for OSNs. We use data sensitivity value and confidence value in targeted group as input variables of fuzzy system. We also use trust values between users since our framework needs to calculate trust loss and gains for reputation value.
Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Information Systems Security and Privacy - Volume 1: ForSE 2019
EditorsPaolo Mori, Steven Furnell, Olivier Camp
PublisherSciTePress
Pages674-684
Number of pages11
ISBN (Print)978-989-758-359-9
DOIs
Publication statusPublished - 23 Feb 2019
Event3rd International Workshop on FORmal methods for Security Engineering - Prague, Czech Republic
Duration: 23 Feb 201925 Feb 2019
http://www.icissp.org/FORSE.aspx

Conference

Conference3rd International Workshop on FORmal methods for Security Engineering
Abbreviated titleForSE 2019
CountryCzech Republic
CityPrague
Period23/02/1925/02/19
Internet address

Documents

  • ICISSP19_Conference_Paper

    Rights statement: Gulsum Akkuzu, Bejamin Aziz, Mo Adda (2019). 'Fuzzy Logic Decision based Collaborative Privacy Management Framework for Online Social Networks,' Proceedings of the 5th International Conference on Information Systems Security and Privacy - Volume 1: ForSE, ISBN 978-989-758-359-9, pages 674-684. DOI: 10.5220/0007702206740684.

    Accepted author manuscript (Post-print), 294 KB, PDF document

Related information

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ID: 13057669