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Privacy dynamics: Learning privacy norms for social software. / Calikli, Gul; Law, Mark; Bandara, Arosha K. et al.
SEAMS '16 Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. New York: Association for Computing Machinery, Inc, 2016. p. 47-56 2897063.
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Calikli, G, Law, M, Bandara, AK, Russo, A, Dickens, L, Price, BA, Stuart, A
, Levine, M & Nuseibeh, B 2016,
Privacy dynamics: Learning privacy norms for social software. in
SEAMS '16 Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems., 2897063, Association for Computing Machinery, Inc, New York, pp. 47-56, 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2016, Austin, United States,
16/05/16.
https://doi.org/10.1145/2897053.2897063APA
Calikli, G., Law, M., Bandara, A. K., Russo, A., Dickens, L., Price, B. A., Stuart, A.
, Levine, M., & Nuseibeh, B. (2016).
Privacy dynamics: Learning privacy norms for social software. In
SEAMS '16 Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (pp. 47-56). Article 2897063 Association for Computing Machinery, Inc.
https://doi.org/10.1145/2897053.2897063Vancouver
Calikli G, Law M, Bandara AK, Russo A, Dickens L, Price BA et al.
Privacy dynamics: Learning privacy norms for social software. In SEAMS '16 Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. New York: Association for Computing Machinery, Inc. 2016. p. 47-56. 2897063 doi: 10.1145/2897053.2897063
Author
Calikli, Gul ; Law, Mark ; Bandara, Arosha K. et al. /
Privacy dynamics : Learning privacy norms for social software. SEAMS '16 Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems. New York : Association for Computing Machinery, Inc, 2016. pp. 47-56
Bibtex
@inproceedings{9aaebb59f27940419f5e0ee4db53813f,
title = "Privacy dynamics: Learning privacy norms for social software",
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.",
keywords = "Adaptive privacy, Inductive logic programming, Online social networks, Social identity theory",
author = "Gul Calikli and Mark Law and Bandara, {Arosha K.} and Alessandra Russo and Luke Dickens and Price, {Blaine A.} and Avelie Stuart and Mark Levine and Bashar Nuseibeh",
year = "2016",
month = may,
day = "14",
doi = "10.1145/2897053.2897063",
language = "English",
pages = "47--56",
booktitle = "SEAMS '16 Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems",
publisher = "Association for Computing Machinery, Inc",
note = "11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2016 ; Conference date: 16-05-2016 Through 17-05-2016",
}
RIS
TY - GEN
T1 - Privacy dynamics
T2 - 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2016
AU - Calikli, Gul
AU - Law, Mark
AU - Bandara, Arosha K.
AU - Russo, Alessandra
AU - Dickens, Luke
AU - Price, Blaine A.
AU - Stuart, Avelie
AU - Levine, Mark
AU - Nuseibeh, Bashar
PY - 2016/5/14
Y1 - 2016/5/14
N2 - 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.
AB - 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.
KW - Adaptive privacy
KW - Inductive logic programming
KW - Online social networks
KW - Social identity theory
U2 - 10.1145/2897053.2897063
DO - 10.1145/2897053.2897063
M3 - Conference contribution/Paper
AN - SCOPUS:84974623403
SP - 47
EP - 56
BT - SEAMS '16 Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
PB - Association for Computing Machinery, Inc
CY - New York
Y2 - 16 May 2016 through 17 May 2016
ER -
