Secure and scalable deduplication of horizontally partitioned health data for privacy-preserving distributed statistical computation
- Submitting institution
-
The University of Westminster
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 9z6vx
- Type
- D - Journal article
- DOI
-
10.1186/s12911-016-0389-x
- Title of journal
- BMC Medical Informatics and Decision Making
- Article number
- 1 (2017)
- First page
- -
- Volume
- 17
- Issue
- -
- ISSN
- 1472-6947
- Open access status
- Compliant
- Month of publication
- January
- 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
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work presented the design and implementation of a secure protocol for the deduplication of horizontally partitioned healthcare datasets with deterministic record linkage algorithms. Apart from its research impact, the designed protocol was deployed and tested across three microbiology laboratories located in Norway. Furthermore, parts of the protocol were adopted by the ASCLEPIOS research project (https://www.asclepios-project.eu/). More precisely, the outputs of this work were used in the design of a multiparty computation protocol that allowed doctors and healthcare practitioners to perform analytics in a privacy-preserving way.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
