A privacy-preserving model to control social interaction behaviors in social network sites
- Submitting institution
-
Abertay University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 23411888
- Type
- D - Journal article
- DOI
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10.1016/j.jisa.2019.102402
- Title of journal
- Journal of Information Security and Applications
- Article number
- 102402
- First page
- -
- Volume
- 49
- Issue
- -
- ISSN
- 2214-2126
- Open access status
- Compliant
- Month of publication
- November
- 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
-
2
- Research group(s)
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C - Cybersecurity
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Network Sites (SNSs) enable online social interactions for more than half a billion users worldwide, providing services via third-party applications. Via SNSs, third parties gain access to users’ profile data, which leads to privacy leakage. The privacy-preserving model developed enhances users’ privacy by providing access to appropriate data without compromising information sharing functionalities. This model has high accuracy in detecting malicious social interactions and provides anonymity to enhance users’ privacy, and led to further funded (RUG-UTM) research into third-party applications.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
