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Volume 13 Issue 2

Confidentiality challenges in releasing longitudinally linked data

Robin Mitra^(a),(*), Stephanie Blanchard^(b), Iain Dove^(b), Caroline Tudor^(b), Keith Spicer^(b)

Transactions on Data Privacy 13:2 (2020) 151 - 170

Abstract, PDF

(a) Department of Mathematics and Statistics, Lancaster University, UK.

(b) Office for National Statistics, Hants. UK.

e-mail:R.Mitra @lancaster.ac.uk; ; ; ;

Abstract

Longitudinally linked household data allows researchers to analyse trends over time as well as on a cross-sectional level. Such analysis requires households to be linked across waves, but this increases the possibility of disclosure risks. We focus on an inter-wave disclosure risk specific to such data sets where intruders can make use of intimate knowledge gained about the household in one wave to learn new sensitive information about the household in future waves. We consider a specific way this risk could occur when households split in one wave, so an individual has left the household, and illustrate this risk using the Wealth and Assets survey. We also show that simply removing the links between waves may be insufficient to adequately protect confidentiality. To mitigate this risk we investigate two statistical disclosure control methods, perturbation and synthesis, that alter sensitive information on these households in the current wave. In this way no new sensitive information will be disclosed to these individuals, while utility should be largely preserved provided the SDC measures are applied appropriately.

^* Corresponding author.

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