Sharing Policies in Multiuser Privacy Scenarios : Incorporating Context, Preferences, and Arguments in Decision Making
- Submitting institution
-
King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 87323253
- Type
- D - Journal article
- DOI
-
10.1145/3038920
- Title of journal
- ACM Transactions on Computer-Human Interaction
- Article number
- 5
- First page
- 1
- Volume
- 24
- Issue
- 1
- ISSN
- 1073-0516
- Open access status
- Compliant
- Month of publication
- March
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- Yes
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This article followed a large-scale (thousands of participants) experimental, regression analysis and machine learning approach to demonstrate that considering the reasons behind user preferences, in the form of arguments, leads significantly more often to predicting the optimal sharing policy in multiuser scenarios. This experimental breakthrough informed the development and architecture of subsequent models for multiuser privacy recommenders by the authors (Fogues et al. IEEE Internet Computing vol. 21, 2017; Mosca et al. PAL-2019) and by other researchers (e.g. Li and Kobsa, JASIST 2020; Murukannaiah et al. AAMAS’20). The article was invited for presentation at CHI’17.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
