K-VARP : K-anonymity for varied data streams via partitioning
- Submitting institution
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University of the West of Scotland
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13118259
- Type
- D - Journal article
- DOI
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10.1016/j.ins.2018.07.057
- Title of journal
- Information Sciences
- Article number
- -
- First page
- 238
- Volume
- 467
- Issue
- -
- ISSN
- 0020-0255
- Open access status
- Compliant
- Month of publication
- August
- Year of publication
- 2018
- 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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3
- Research group(s)
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-
- Citation count
- 6
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This novel work proposes K-VARP (K-anonymity for VARied data stream via Partitioning) to publish varied data stream. It is one of the first significant attempt to anonymise IoT streaming data with missing values – addressing challenges of privacy-aware data usability. K-VARP reads tuple and assigns them to partitions using their description, and all tuples must be anonymized before expiring. It proposes new merging criterion (R-likeness) to measure similarity distance between tuple and partitions - flexible re-using and imputation free-publication has helped to achieve better anonymization quality and performance.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -