MERLIN-A Tool for Multi-party Privacy-Preserving Record Linkage

Thilina Ranbaduge; Dinusha Vatsalan; Peter Christen

doi:10.1109/ICDMW.2015.101

MERLIN-A Tool for Multi-party Privacy-Preserving Record Linkage

Thilina Ranbaduge, Dinusha Vatsalan, Peter Christen

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

3 Citations (Scopus)

Abstract

Many organizations, including businesses, government agencies and research organizations, are collecting vast amounts of data, which are stored, processed and analyzed to mine interesting patterns and knowledge to support efficient and quality decision making. In order to improve data quality and to facilitate further analysis, many application domains require information from multiple sources to be integrated and combined. The process of matching and aggregating records that relate to the same entities from different data sources without compromising their privacy is known as 'privacy-preserving record linkage' (PPRL), 'blind data linkage' or 'private record linkage'. In this paper we present MERLIN, an online tool that demonstrates various PPRL methods in a multi-party context. In this demonstration we show different private multi-party blocking and matching techniques, and illustrate the usability of MERLIN by presenting quality and performance measures of various PPRL methods. We believe MERLIN will help practitioners and researchers to better understand the pipeline of the PPRL process, to compare different multi-party PPRL techniques, and to determine the best technique to use for their needs.

Original language	English
Title of host publication	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Editors	Xindong Wu, Alexander Tuzhilin, Hui Xiong, Jennifer G. Dy, Charu Aggarwal, Zhi-Hua Zhou, Peng Cui
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1640-1643
Number of pages	4
ISBN (Electronic)	9781467384926
DOIs	https://doi.org/10.1109/ICDMW.2015.101
Publication status	Published - 29 Jan 2016
Event	15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 - Atlantic City, United States Duration: 14 Nov 2015 → 17 Nov 2015

Publication series

Name	Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

Conference

Conference	15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Country/Territory	United States
City	Atlantic City
Period	14/11/15 → 17/11/15

Access to Document

10.1109/ICDMW.2015.101

Cite this

Ranbaduge, T., Vatsalan, D., & Christen, P. (2016). MERLIN-A Tool for Multi-party Privacy-Preserving Record Linkage. In X. Wu, A. Tuzhilin, H. Xiong, J. G. Dy, C. Aggarwal, Z.-H. Zhou, & P. Cui (Eds.), Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 (pp. 1640-1643). Article 7395877 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDMW.2015.101

Ranbaduge, Thilina ; Vatsalan, Dinusha ; Christen, Peter. / MERLIN-A Tool for Multi-party Privacy-Preserving Record Linkage. Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. editor / Xindong Wu ; Alexander Tuzhilin ; Hui Xiong ; Jennifer G. Dy ; Charu Aggarwal ; Zhi-Hua Zhou ; Peng Cui. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 1640-1643 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015).

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title = "MERLIN-A Tool for Multi-party Privacy-Preserving Record Linkage",

abstract = "Many organizations, including businesses, government agencies and research organizations, are collecting vast amounts of data, which are stored, processed and analyzed to mine interesting patterns and knowledge to support efficient and quality decision making. In order to improve data quality and to facilitate further analysis, many application domains require information from multiple sources to be integrated and combined. The process of matching and aggregating records that relate to the same entities from different data sources without compromising their privacy is known as 'privacy-preserving record linkage' (PPRL), 'blind data linkage' or 'private record linkage'. In this paper we present MERLIN, an online tool that demonstrates various PPRL methods in a multi-party context. In this demonstration we show different private multi-party blocking and matching techniques, and illustrate the usability of MERLIN by presenting quality and performance measures of various PPRL methods. We believe MERLIN will help practitioners and researchers to better understand the pipeline of the PPRL process, to compare different multi-party PPRL techniques, and to determine the best technique to use for their needs.",

keywords = "Bloom filters, Data matching, Online demo, Privacy, Scalability",

author = "Thilina Ranbaduge and Dinusha Vatsalan and Peter Christen",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 ; Conference date: 14-11-2015 Through 17-11-2015",

year = "2016",

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doi = "10.1109/ICDMW.2015.101",

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Ranbaduge, T, Vatsalan, D & Christen, P 2016, MERLIN-A Tool for Multi-party Privacy-Preserving Record Linkage. in X Wu, A Tuzhilin, H Xiong, JG Dy, C Aggarwal, Z-H Zhou & P Cui (eds), Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015., 7395877, Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Institute of Electrical and Electronics Engineers Inc., pp. 1640-1643, 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Atlantic City, United States, 14/11/15. https://doi.org/10.1109/ICDMW.2015.101

MERLIN-A Tool for Multi-party Privacy-Preserving Record Linkage. / Ranbaduge, Thilina; Vatsalan, Dinusha; Christen, Peter.
Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. ed. / Xindong Wu; Alexander Tuzhilin; Hui Xiong; Jennifer G. Dy; Charu Aggarwal; Zhi-Hua Zhou; Peng Cui. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1640-1643 7395877 (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015).

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

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AU - Ranbaduge, Thilina

AU - Vatsalan, Dinusha

AU - Christen, Peter

PY - 2016/1/29

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N2 - Many organizations, including businesses, government agencies and research organizations, are collecting vast amounts of data, which are stored, processed and analyzed to mine interesting patterns and knowledge to support efficient and quality decision making. In order to improve data quality and to facilitate further analysis, many application domains require information from multiple sources to be integrated and combined. The process of matching and aggregating records that relate to the same entities from different data sources without compromising their privacy is known as 'privacy-preserving record linkage' (PPRL), 'blind data linkage' or 'private record linkage'. In this paper we present MERLIN, an online tool that demonstrates various PPRL methods in a multi-party context. In this demonstration we show different private multi-party blocking and matching techniques, and illustrate the usability of MERLIN by presenting quality and performance measures of various PPRL methods. We believe MERLIN will help practitioners and researchers to better understand the pipeline of the PPRL process, to compare different multi-party PPRL techniques, and to determine the best technique to use for their needs.

AB - Many organizations, including businesses, government agencies and research organizations, are collecting vast amounts of data, which are stored, processed and analyzed to mine interesting patterns and knowledge to support efficient and quality decision making. In order to improve data quality and to facilitate further analysis, many application domains require information from multiple sources to be integrated and combined. The process of matching and aggregating records that relate to the same entities from different data sources without compromising their privacy is known as 'privacy-preserving record linkage' (PPRL), 'blind data linkage' or 'private record linkage'. In this paper we present MERLIN, an online tool that demonstrates various PPRL methods in a multi-party context. In this demonstration we show different private multi-party blocking and matching techniques, and illustrate the usability of MERLIN by presenting quality and performance measures of various PPRL methods. We believe MERLIN will help practitioners and researchers to better understand the pipeline of the PPRL process, to compare different multi-party PPRL techniques, and to determine the best technique to use for their needs.

KW - Bloom filters

KW - Data matching

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KW - Scalability

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BT - Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

A2 - Wu, Xindong

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A2 - Dy, Jennifer G.

A2 - Aggarwal, Charu

A2 - Zhou, Zhi-Hua

A2 - Cui, Peng

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

Y2 - 14 November 2015 through 17 November 2015

ER -

Ranbaduge T, Vatsalan D, Christen P. MERLIN-A Tool for Multi-party Privacy-Preserving Record Linkage. In Wu X, Tuzhilin A, Xiong H, Dy JG, Aggarwal C, Zhou ZH, Cui P, editors, Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 1640-1643. 7395877. (Proceedings - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015). doi: 10.1109/ICDMW.2015.101

MERLIN-A Tool for Multi-party Privacy-Preserving Record Linkage

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