Towards a general record linkage framework for statistical disclosure control

Duncan Smith; Mark Elliot

doi:10.1145/2970030.2970037

Towards a general record linkage framework for statistical disclosure control

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	PrAISe 2016 - 1st International Workshop on Artificial Intelligence for Privacy and Security, Held at EUMAS 2016
Publisher	Association for Computing Machinery
Volume	29-30-August-2016
ISBN (Electronic)	1595930361, 9781450343046
DOIs	https://doi.org/10.1145/2970030.2970037
Publication status	Published - 29 Aug 2016
Event	1st International Workshop on Artificial Intelligence for Privacy and Security, PrAISe 2016 - The Hague, Netherlands Duration: 29 Aug 2016 → 30 Aug 2016

Conference

Conference	1st International Workshop on Artificial Intelligence for Privacy and Security, PrAISe 2016
Country/Territory	Netherlands
City	The Hague
Period	29/08/16 → 30/08/16

Keywords

Bayes
Record linkage
Statistical disclosure

Research Beacons, Institutes and Platforms

Manchester Cancer Research Centre

Access to Document

10.1145/2970030.2970037Licence: CC BY

Cite this

@inproceedings{0aa63f3fb1e444e084c58a66d07e4b1e,

title = "Towards a general record linkage framework for statistical disclosure control",

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.",

keywords = "Bayes, Record linkage, Statistical disclosure",

author = "Duncan Smith and Mark Elliot",

year = "2016",

month = aug,

day = "29",

doi = "10.1145/2970030.2970037",

language = "English",

volume = "29-30-August-2016",

booktitle = "PrAISe 2016 - 1st International Workshop on Artificial Intelligence for Privacy and Security, Held at EUMAS 2016",

publisher = "Association for Computing Machinery",

address = "United States",

note = "1st International Workshop on Artificial Intelligence for Privacy and Security, PrAISe 2016 ; Conference date: 29-08-2016 Through 30-08-2016",

}

Smith, D & Elliot, M 2016, Towards a general record linkage framework for statistical disclosure control. in PrAISe 2016 - 1st International Workshop on Artificial Intelligence for Privacy and Security, Held at EUMAS 2016. vol. 29-30-August-2016, 2970037, Association for Computing Machinery, 1st International Workshop on Artificial Intelligence for Privacy and Security, PrAISe 2016, The Hague, Netherlands, 29/08/16. https://doi.org/10.1145/2970030.2970037

Towards a general record linkage framework for statistical disclosure control. / Smith, Duncan; Elliot, Mark.
PrAISe 2016 - 1st International Workshop on Artificial Intelligence for Privacy and Security, Held at EUMAS 2016. Vol. 29-30-August-2016 Association for Computing Machinery, 2016. 2970037.

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Towards a general record linkage framework for statistical disclosure control

AU - Smith, Duncan

AU - Elliot, Mark

PY - 2016/8/29

Y1 - 2016/8/29

N2 - 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.

AB - 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.

KW - Bayes

KW - Record linkage

KW - Statistical disclosure

UR - http://www.scopus.com/inward/record.url?scp=84989261712&partnerID=8YFLogxK

U2 - 10.1145/2970030.2970037

DO - 10.1145/2970030.2970037

M3 - Conference contribution

AN - SCOPUS:84989261712

VL - 29-30-August-2016

BT - PrAISe 2016 - 1st International Workshop on Artificial Intelligence for Privacy and Security, Held at EUMAS 2016

PB - Association for Computing Machinery

T2 - 1st International Workshop on Artificial Intelligence for Privacy and Security, PrAISe 2016

Y2 - 29 August 2016 through 30 August 2016

ER -

Towards a general record linkage framework for statistical disclosure control

Abstract

Conference

Keywords

Research Beacons, Institutes and Platforms

Access to Document

Other files and links

Fingerprint

National Centre for Research Methods 2014-2019

Cite this

Towards a general record linkage framework for statistical disclosure control

Abstract

Conference

Keywords

Research Beacons, Institutes and Platforms

Access to Document

Other files and links

Fingerprint

Projects

National Centre for Research Methods 2014-2019

Cite this