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Privacy Dynamics: Learning Privacy Norms for Social Software

Calikli, Gul; Law, Mark; Bandara, Arosha K.; Russo, Alesandra; Dickens, Luke; Price, Blaine A.; Stuart, Avelie; Levine, Mark and Nuseibeh, Bashar (2016). Privacy Dynamics: Learning Privacy Norms for Social Software. In: 2016 IEEE/ACM 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, Association of Computing Machinery pp. 47–56.

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URL: http://seams2016.jgreen.de/?page_id=35
DOI (Digital Object Identifier) Link: https://doi.org/10.1145/2897053.2897063
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Abstract

Privacy violations in online social networks (OSNs) often arise as a result of users sharing information with unintended audiences. One reason for this is that, although OSN capabilities for creating and managing social groups can make it easier to be selective about recipients of a given post, they do not provide enough guidance to the users to make informed sharing decisions. In this paper we present Privacy Dynamics, an adaptive architecture that learns privacy norms for different audience groups based on users’ sharing behaviours. Our architecture is underpinned by a formal model inspired by social identity theory, a social psychology framework for analysing group processes and intergroup relations. Our formal model comprises two main concepts, the group membership as a Social Identity (SI) map and privacy norms as a set of conflict rules. In our approach a privacy norm is specified in terms of the information objects that should be prevented from flowing between two conflicting social identity groups. We implement our formal model by using inductive logic programming (ILP), which automatically learns privacy norms. We evaluate the performance of our learning approach using synthesised data representing the sharing behaviour of social network users.

Item Type: Conference or Workshop Item
Copyright Holders: 2016 Association of Computing Machinery
ISBN: 1-4503-4187-X, 978-1-4503-4187-5
Project Funding Details:
Funded Project NameProject IDFunding Body
Privacy Dynamics: Learning from the wisdom of groups (XC-12-062-BN)EP/K033522/1EPSRC (Engineering and Physical Sciences Research Council)
Adaptive Security And Privacy (XC-11-004-BN)291652EC (European Commission): FP (inc.Horizon2020 & ERC schemes)
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Interdisciplinary Research Centre: Centre for Policing Research and Learning (CPRL)
Centre for Research in Computing (CRC)
International Development & Inclusive Innovation
International Centre for Comparative Criminological Research (ICCCR)
Health and Wellbeing PRA (Priority Research Area)
Item ID: 45951
Depositing User: Arosha Bandara
Date Deposited: 06 Apr 2016 15:32
Last Modified: 16 Aug 2017 15:51
URI: http://oro.open.ac.uk/id/eprint/45951
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