Resolving Multi-Party Privacy Conflicts in Social Media
- Submitting institution
-
King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 87323491
- Type
- D - Journal article
- DOI
-
10.1109/TKDE.2016.2539165
- Title of journal
- IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Article number
- -
- First page
- 1851
- Volume
- 28
- Issue
- 7
- ISSN
- 1041-4347
- Open access status
- Out of scope for open access requirements
- Month of publication
- March
- Year of publication
- 2016
- 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
-
1
- Research group(s)
-
-
- Citation count
- 32
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This article represents a fundamental rethinking of multiparty privacy, as the first to consider and formally model the social factors in this domain, an aspect previously neglected with consequent poor results in practice with users. The article, following a rigorous methodology combining formal proofs and user studies, demonstrated that recommendations provided by the model were accepted significantly more often by social media users compared with previous approaches. This article led to invited talks at Google Mountain View, University of Oxford and University of Lausanne, and featured in The Huffington Post (https://www.huffingtonpost.co.uk/entry/experimental-algorithm-could-stopembarrassing- photos-appearing-on-facebook_uk_56e2830be4b05c52666e7cb1).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -