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

Shlomo, Natalie and Skinner, Chris (2022) Measuring risk of re-identification in microdata: state-of-the art and new directions. Journal of the Royal Statistical Society. Series A: Statistics in Society, 185 (4). 1644 - 1662. ISSN 0964-1998

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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.

Item Type:	Article
Official URL:	https://rss.onlinelibrary.wiley.com/journal/146798...
Additional Information:	© 2022 The Author(s).
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	24 Oct 2022 13:27
Last Modified:	16 Nov 2024 22:57
URI:	http://eprints.lse.ac.uk/id/eprint/117168

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