Towards a general record linkage framework for statistical disclosure control

Duncan Smith, Mark Elliot

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The assessment of statistical disclosure risk often requires the linking of data. There are effective means of linking data for simple scenarios; but it is not clear how best to approach linkage for more complex scenarios. We examine linkage approaches for three simple scenarios and argue that they might be combined.

Original languageEnglish
Title of host publicationPrAISe 2016 - 1st International Workshop on Artificial Intelligence for Privacy and Security, Held at EUMAS 2016
PublisherAssociation for Computing Machinery
Volume29-30-August-2016
ISBN (Electronic)1595930361, 9781450343046
DOIs
Publication statusPublished - 29 Aug 2016
Event1st International Workshop on Artificial Intelligence for Privacy and Security, PrAISe 2016 - The Hague, Netherlands
Duration: 29 Aug 201630 Aug 2016

Conference

Conference1st International Workshop on Artificial Intelligence for Privacy and Security, PrAISe 2016
Country/TerritoryNetherlands
CityThe Hague
Period29/08/1630/08/16

Keywords

  • Bayes
  • Record linkage
  • Statistical disclosure

Research Beacons, Institutes and Platforms

  • Manchester Cancer Research Centre

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