A Probabilistic Procedure for Anonymisation, for Assessing the Risk of Re-identification and for the Analysis of Perturbed Data Sets

Goldstein Harvey () and Shlomo Natalie ()
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Goldstein Harvey: Graduate School of Education, University of Bristol, Bristol, BS8 1JA, UK.
Shlomo Natalie: University of Manchester, Social Statistics, Humanities Bridgeford Street Manchester, M13 9PL, UK.

Journal of Official Statistics, 2020, vol. 36, issue 1, 89-115

Abstract: The requirement to anonymise data sets that are to be released for secondary analysis should be balanced by the need to allow their analysis to provide efficient and consistent parameter estimates. The proposal in this article is to integrate the process of anonymisation and data analysis. The first stage uses the addition of random noise with known distributional properties to some or all variables in a released (already pseudonymised) data set, in which the values of some identifying and sensitive variables for data subjects of interest are also available to an external ‘attacker’ who wishes to identify those data subjects in order to interrogate their records in the data set. The second stage of the analysis consists of specifying the model of interest so that parameter estimation accounts for the added noise. Where the characteristics of the noise are made available to the analyst by the data provider, we propose a new method that allows a valid analysis. This is formally a measurement error model and we describe a Bayesian MCMC algorithm that recovers consistent estimates of the true model parameters. A new method for handling categorical data is presented. The article shows how an appropriate noise distribution can be determined.

Keywords: Additive noise; anonymisation; measurement error; record linkage (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:36:y:2020:i:1:p:89-115:n:5

DOI: 10.2478/jos-2020-0005

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