Measuring risk of re-identification in microdata: state-of-the art and new directions

Natalie Shlomo and Chris Skinner

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We review the influential research carried out by Chris Skinner in the area of statistical disclosure control, and in particular quantifying the risk of re-identification in sample microdata from a random survey drawn from a finite population. We use the sample microdata to infer population parameters when the population is unknown, and estimate the risk of re-identification based on the notion of population uniqueness using probabilistic modelling. We also introduce a new approach to measure the risk of re-identification for a subpopulation in a register that is not representative of the general population, for example a register of cancer patients. In addition, we can use the additional information from the register to measure the risk of re-identification for the sample microdata. This new approach was developed by the two authors and is published here for the first time. We demonstrate this approach in an application study based on UK census data where we can compare the estimated risk measures to the known truth.

Keywords: disclosure risks; key variables; log-linear models; model specification; probability scores estimation; registers (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2022-10-07
New Economics Papers: this item is included in nep-ecm
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Published in Journal of the Royal Statistical Society. Series A: Statistics in Society, 7, October, 2022, 185(4), pp. 1644 - 1662. ISSN: 0964-1998

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