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Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Learning to share
T2 - 32nd IEEE/ACM International Conference on Automated Software Engineering, ASE 2017
AU - Rafiq, Yasmin
AU - Dickens, Luke
AU - Russo, Alessandra
AU - Bandara, Arosha K.
AU - Yang, Mu
AU - Stuart, Avelie
AU - Levine, Mark
AU - Calikli, Gul
AU - Price, Blaine A.
AU - Nuseibeh, Bashar
N1 - ©2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
PY - 2017/11/20
Y1 - 2017/11/20
N2 - Some online social networks (OSNs) allow users to define friendship-groups as reusable shortcuts for sharing information with multiple contacts. Posting exclusively to a friendship-group gives some privacy control, while supporting communication with (and within) this group. However, recipients of such posts may want to reuse content for their own social advantage, and can bypass existing controls by copy-pasting into a new post; this cross-posting poses privacy risks. This paper presents a learning to share approach that enables the incorporation of more nuanced privacy controls into OSNs. Specifically, we propose a reusable, adaptive software architecture that uses rigorous runtime analysis to help OSN users to make informed decisions about suitable audiences for their posts. This is achieved by supporting dynamic formation of recipient-groups that benefit social interactions while reducing privacy risks. We exemplify the use of our approach in the context of Facebook.
AB - Some online social networks (OSNs) allow users to define friendship-groups as reusable shortcuts for sharing information with multiple contacts. Posting exclusively to a friendship-group gives some privacy control, while supporting communication with (and within) this group. However, recipients of such posts may want to reuse content for their own social advantage, and can bypass existing controls by copy-pasting into a new post; this cross-posting poses privacy risks. This paper presents a learning to share approach that enables the incorporation of more nuanced privacy controls into OSNs. Specifically, we propose a reusable, adaptive software architecture that uses rigorous runtime analysis to help OSN users to make informed decisions about suitable audiences for their posts. This is achieved by supporting dynamic formation of recipient-groups that benefit social interactions while reducing privacy risks. We exemplify the use of our approach in the context of Facebook.
U2 - 10.1109/ASE.2017.8115641
DO - 10.1109/ASE.2017.8115641
M3 - Conference contribution/Paper
AN - SCOPUS:85041441639
SP - 280
EP - 285
BT - ASE 2017 - Proceedings of the 32nd IEEE/ACM International Conference on Automated Software Engineering
A2 - Nguyen, Tien N.
A2 - Rosu, Grigore
A2 - Di Penta, Massimiliano
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 30 October 2017 through 3 November 2017
ER -