Blind data linkage using n-gram similarity comparisons

Tim Churches; Peter Christen

doi:10.1007/978-3-540-24775-3_15

Blind data linkage using n-gram similarity comparisons

Tim Churches, Peter Christen^*

^*Corresponding author for this work

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

17 Citations (Scopus)

Abstract

Integrating or linking data from different sources is an increasingly important task in the preprocessing stage of many data mining projects. The aim of such linkages is to merge all records relating to the same entity, such as a patient or a customer. If no common unique entity identifiers (keys) are available in all data sources, the linkage needs to be performed using the available identifying attributes, like names and addresses. Data confidentiality often limits or even prohibits successful data linkage, as either no consent can be gained (for example in biomedical studies) or the data holders are not willing to release their data for linkage by other parties. We present methods for confidential data linkage based on hash encoding, public key encryption and n-gram similarity comparison techniques, and show how blind data linkage can be performed.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings
Editors	Honghua Dai, Ramakrishnan Srikant, Chengqi Zhang
Publisher	Springer Verlag
Pages	121-126
Number of pages	6
ISBN (Print)	354022064X, 9783540220640
DOIs	https://doi.org/10.1007/978-3-540-24775-3_15
Publication status	Published - 2004
Event	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia Duration: 26 May 2004 → 28 May 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3056
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004
Country/Territory	Australia
City	Sydney
Period	26/05/04 → 28/05/04

Access to Document

10.1007/978-3-540-24775-3_15

Cite this

Churches, T., & Christen, P. (2004). Blind data linkage using n-gram similarity comparisons. In H. Dai, R. Srikant, & C. Zhang (Eds.), Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings (pp. 121-126). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3056). Springer Verlag. https://doi.org/10.1007/978-3-540-24775-3_15

Churches, Tim ; Christen, Peter. / Blind data linkage using n-gram similarity comparisons. Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. editor / Honghua Dai ; Ramakrishnan Srikant ; Chengqi Zhang. Springer Verlag, 2004. pp. 121-126 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Integrating or linking data from different sources is an increasingly important task in the preprocessing stage of many data mining projects. The aim of such linkages is to merge all records relating to the same entity, such as a patient or a customer. If no common unique entity identifiers (keys) are available in all data sources, the linkage needs to be performed using the available identifying attributes, like names and addresses. Data confidentiality often limits or even prohibits successful data linkage, as either no consent can be gained (for example in biomedical studies) or the data holders are not willing to release their data for linkage by other parties. We present methods for confidential data linkage based on hash encoding, public key encryption and n-gram similarity comparison techniques, and show how blind data linkage can be performed.",

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Churches, T & Christen, P 2004, Blind data linkage using n-gram similarity comparisons. in H Dai, R Srikant & C Zhang (eds), Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3056, Springer Verlag, pp. 121-126, 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004, Sydney, Australia, 26/05/04. https://doi.org/10.1007/978-3-540-24775-3_15

Blind data linkage using n-gram similarity comparisons. / Churches, Tim; Christen, Peter.
Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. ed. / Honghua Dai; Ramakrishnan Srikant; Chengqi Zhang. Springer Verlag, 2004. p. 121-126 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3056).

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

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AB - Integrating or linking data from different sources is an increasingly important task in the preprocessing stage of many data mining projects. The aim of such linkages is to merge all records relating to the same entity, such as a patient or a customer. If no common unique entity identifiers (keys) are available in all data sources, the linkage needs to be performed using the available identifying attributes, like names and addresses. Data confidentiality often limits or even prohibits successful data linkage, as either no consent can be gained (for example in biomedical studies) or the data holders are not willing to release their data for linkage by other parties. We present methods for confidential data linkage based on hash encoding, public key encryption and n-gram similarity comparison techniques, and show how blind data linkage can be performed.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Churches T, Christen P. Blind data linkage using n-gram similarity comparisons. In Dai H, Srikant R, Zhang C, editors, Advances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings. Springer Verlag. 2004. p. 121-126. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-24775-3_15

Blind data linkage using n-gram similarity comparisons

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