Papers 2018

Anonymisation with noise applications_

Abstract
Individual-level data require protection from unauthorised access. Effective anonymisation techniques should minimise the probability of re-identification while allowing informative data analysis. ‘Probabilistic anonymisation’ alters the data by addition of random noise. In this paper, we describe the implementation of one such technique and demonstrate its application to analysis of asthma-related data from the ALSPAC cohort study.

Using weights in a Bayesian modelling framework

Pseudonymisation at source