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A software package for the application of probabilistic anonymisation to sensitive individual-level data: A proof of principle with an example from the ALSPAC birth cohort study
Avraam, Demetris Boyd, Andy Goldstein, Harvey Burton, Paul
Avraam, Demetris
Boyd, Andy
Goldstein, Harvey
Burton, Paul
Abstract
Individual-level data require protection from unauthorised access to safeguard confidentiality and security of sensitive information. Risks of disclosure are evaluated through privacy risk assessments and are controlled or minimised before data sharing and integration. The evolution from ‘Micro Data Laboratory’ traditions (i.e. access in controlled physical locations) to ‘Open Data’ (i.e. sharing individual-level data) drives the development of efficient anonymisation methods and protection controls. Effective anonymisation techniques should increase the uncertainty surrounding re-identification while retaining data utility, allowing informative data analysis. ‘Probabilistic anonymisation’ is one such technique, which alters the data by addition of random noise. In this paper, we describe the implementation of one probabilistic anonymisation technique into an operational software written in R and we demonstrate its applicability through application to analysis of asthma-related data from the ALSPAC cohort study. The software is designed to be used by data managers and users without the requirement of advanced statistical knowledge.
Keywords
probabilistic anonymisation, disclosure control, measurement error, h-rank index, ALSPAC
Date
2018
Type
Journal article
Journal
Longitudinal and Life Course Studies
Book
Volume
9
Issue
4
Page Range
433-446
Article Number
ACU Department
Collections
Files
Relation URI
Source URL
Event URL
Open Access Status
Open access
License
File Access
Open
Controlled
Controlled
Notes
Bronze open access.
This record includes a post-peer-review, pre-copy edited version of an article published in Longitudinal and Life Course Studies. The definitive publisher-authenticated version Avraam, D., Boyd, A., Goldstein, H., & Burton, P. (2018). A software package for the application of probabilistic anonymisation to sensitive individual-level data: a proof of principle with an example from the ALSPAC birth cohort study. Longitudinal and Life Course Studies, 9(4), 433-446, is available at: doi:http://dx.doi.org/10.14301/llcs.v9i4.478
This record includes a post-peer-review, pre-copy edited version of an article published in Longitudinal and Life Course Studies. The definitive publisher-authenticated version Avraam, D., Boyd, A., Goldstein, H., & Burton, P. (2018). A software package for the application of probabilistic anonymisation to sensitive individual-level data: a proof of principle with an example from the ALSPAC birth cohort study. Longitudinal and Life Course Studies, 9(4), 433-446, is available at: doi:http://dx.doi.org/10.14301/llcs.v9i4.478