期刊
LONGITUDINAL AND LIFE COURSE STUDIES
卷 9, 期 4, 页码 433-446出版社
SOC LONGITUDINAL & LIFE COURSE STUDIES
DOI: 10.14301/llcs.v9i4.478
关键词
Probabilistic anonymisation; disclosure control; measurement error; h-rank index; ALSPAC
资金
- Wellcome Trust [102215/2/13/2]
- UK Economic and Social Research Council and Medical Research Council [ES/K000357/1]
- Department of Health and Social Care
- ESRC [ES/K000357/1] Funding Source: UKRI
- MRC [MR/K006525/1, MR/K007017/1] Funding Source: UKRI
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.
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