Privacy dynamics: learning privacy norms for social software
- Submitting institution
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The Open University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1587392
- Type
- E - Conference contribution
- DOI
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10.1145/2897053.2897063
- Title of conference / published proceedings
- 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems
- First page
- 47
- Volume
- -
- Issue
- -
- ISSN
- 2157-2321
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2016
- URL
-
-
- Supplementary information
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-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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8
- Research group(s)
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-
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This successful collaboration between SE, Social Psychology and AI researchers presents a novel approach to integrating the concept of social identity into privacy-aware adaptive software architectures, based on application of hybrid inductive learning techniques. The architecture was a key result of the EPSRC Privacy Dynamics project (EP/K033522/1), and underpinned subsequent work on adaptive sharing in social media systems. It is cited by researchers at CHI as well as in IJHCS and ACM TOIT showing successful learning of privacy norms based on social groups; and the methodology presented in the paper was used in the doctoral research of Misra [Lancaster].
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -