Information Theoretical Analysis of Unfair Rating Attacks under Subjectivity
- Submitting institution
-
University of Nottingham, The
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2286238
- Type
- D - Journal article
- DOI
-
10.1109/TIFS.2019.2929678
- Title of journal
- IEEE Transactions on Information Forensics and Security
- Article number
- -
- First page
- 816
- Volume
- 15
- Issue
- -
- ISSN
- 1556-6013
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- 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
-
3
- Research group(s)
-
-
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Probably honest online ratings are more informative than probably manipulative ones, and this paper formally establishes a theoretical underpinning of this idea, while considering the possibility of honest mistakes or differences in viewpoint. The method allows one to establish whether online ratings are a useful endeavour under given circumstances. Machine learning algorithms learning correlations between ratings are particularly open to manipulation, in adversarial circumstances. Given that such algorithms play an increasingly important role, a mathematical understanding of information content of online ratings in an adversarial setting is crucial.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
