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Trust And Privacy Correlations in Social Networks: A Deep Learning Framework
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0002-7786-9551
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.ORCID iD: 0000-0002-4722-0823
2016 (English)In: PROCEEDINGS OF THE 2016 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING ASONAM 2016, IEEE conference proceedings, 2016, p. 203-206Conference paper, Published paper (Refereed)
Abstract [en]

Online Social Networks (OSNs) remain the focal point of Internet usage. Since the beginning, networking sites tried best to have right privacy mechanisms in place for users, enabling them to share the right content with the right audience. With all these efforts, privacy customizations remain hard for users across the sites. Existing research that address this problem mainly focus on semi-supervised strategies that introduce extra complexity by requiring the user to manually specify initial privacy preferences for their friends. In this work, we suggest an adaptive solution that can dynamically generate privacy labels for users in OSNs. To this end, we introduce a deep reinforcement learning framework that targets two key problems in OSNs like Facebook: the exposure of users' interactions through the network to less trusted direct friends, and the possibility of propagating user updates through direct friends' interactions to indirect friends. By implementing this framework, we aim at understanding how social trust and privacy could be correlated, specifically in a dynamic fashion. We report the ranked dependence between the generated privacy labels and the estimated user trust values, which indicate the ability of the framework to identify the highly trusted users and share with them higher percentages of data.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. p. 203-206
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-200264ISI: 000390760100031Scopus ID: 2-s2.0-85006765626ISBN: 978-1-5090-2846-7 (print)OAI: oai:DiVA.org:kth-200264DiVA, id: diva2:1069753
Conference
8th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), AUG 18-21, 2016, San Francisco, CA
Note

QC 20170130

Available from: 2017-01-30 Created: 2017-01-23 Last updated: 2018-01-13Bibliographically approved

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No full text in DiVA

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Scopushttp://asonam.cpsc.ucalgary.ca/2016/CFP.php

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Matskin, Mihhail

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