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Automated zone design as an aid to effective anonymisation

Automated zone design as an aid to effective anonymisation
Automated zone design as an aid to effective anonymisation
Geographical aggregation is a long-established tool in the effective anonymisation of personal data records. This applies both to the preparation of national datasets for open publication, such as small area census statistics, and to the protection of microdata, such as the limitation on geographical coding in the ONS Longitudinal Study to Government Office Region level. Choice of geographical aggregation scale is likely to interact with access controls and other aspects of the analysis environment (e.g. highly controlled access environments for microdata). A key risk with individual records is that an intruder may be able to use external data relating to the same geographical units in order to identify (find someone) or attribute characteristics (learn new information about someone). A central challenge therefore is the determination of aggregation levels which provide sufficient protection without excessively affecting utility from the users’ perspective. This presentation considers the contribution of automated zone design to effective anonymization, ranging from its use in 2011 Census data production by the Office for National Statistics (ONS) to current work at the Economic and Social Research Council (ESRC) funded National Centre for Research Methods (NCRM) using synthetic data to explore bespoke aggregation scenarios for use in protection of linked administrative data
Robards, James
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Martin, David
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Gale, Chris
5e6578ce-b9cf-4173-aad8-4c5cbd6c3696
Robards, James
4c79fa72-e722-4a2a-a289-1d2bad2c2343
Martin, David
e5c52473-e9f0-4f09-b64c-fa32194b162f
Gale, Chris
5e6578ce-b9cf-4173-aad8-4c5cbd6c3696

Robards, James, Martin, David and Gale, Chris (2016) Automated zone design as an aid to effective anonymisation. At New Approaches to Anonymisation New Approaches to Anonymisation, United Kingdom.

Record type: Conference or Workshop Item (Other)

Abstract

Geographical aggregation is a long-established tool in the effective anonymisation of personal data records. This applies both to the preparation of national datasets for open publication, such as small area census statistics, and to the protection of microdata, such as the limitation on geographical coding in the ONS Longitudinal Study to Government Office Region level. Choice of geographical aggregation scale is likely to interact with access controls and other aspects of the analysis environment (e.g. highly controlled access environments for microdata). A key risk with individual records is that an intruder may be able to use external data relating to the same geographical units in order to identify (find someone) or attribute characteristics (learn new information about someone). A central challenge therefore is the determination of aggregation levels which provide sufficient protection without excessively affecting utility from the users’ perspective. This presentation considers the contribution of automated zone design to effective anonymization, ranging from its use in 2011 Census data production by the Office for National Statistics (ONS) to current work at the Economic and Social Research Council (ESRC) funded National Centre for Research Methods (NCRM) using synthetic data to explore bespoke aggregation scenarios for use in protection of linked administrative data

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More information

Submitted date: 15 September 2016
e-pub ahead of print date: 5 December 2016
Venue - Dates: New Approaches to Anonymisation, United Kingdom, 2016-12-05
Organisations: Social Statistics & Demography

Identifiers

Local EPrints ID: 403691
URI: https://eprints.soton.ac.uk/id/eprint/403691
PURE UUID: 38faebf7-784e-474d-ad62-9ac9968bc8d7
ORCID for James Robards: ORCID iD orcid.org/0000-0003-4784-5679
ORCID for David Martin: ORCID iD orcid.org/0000-0003-0397-0769

Catalogue record

Date deposited: 09 Dec 2016 09:26
Last modified: 06 Jun 2018 13:09

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Contributors

Author: James Robards ORCID iD
Author: David Martin ORCID iD
Author: Chris Gale

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