Hashing-based distributed multi-party blocking for privacy-preserving record linkage

Thilina Ranbaduge; Dinusha Vatsalan; Peter Christen; Vassilios Verykios

doi:10.1007/978-3-319-31750-2_33

Hashing-based distributed multi-party blocking for privacy-preserving record linkage

Thilina Ranbaduge^*, Dinusha Vatsalan, Peter Christen, Vassilios Verykios

^*Corresponding author for this work

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

12 Citations (Scopus)

Abstract

In many application domains organizations require information from multiple sources to be integrated. Due to privacy and confidentiality concerns often these organizations are not willing or allowed to reveal their sensitive and personal data to other database owners, and to any external party. This has led to the emerging research discipline of privacy-preserving record linkage (PPRL). We propose a novel blocking approach for multi-party PPRL to efficiently and effectively prune the record sets that are unlikely to match. Our approach allows each database owner to perform blocking independently except for the initial agreement of parameter settings and a final central hashing-based clustering. We provide an analysis of our technique in terms of complexity, quality, and privacy, and conduct an empirical study with large datasets. The results show that our approach is scalable with the size of the datasets and the number of parties, while providing better quality and privacy than previous multi-party private blocking approaches.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings
Editors	James Bailey, Latifur Khan, Takashi Washio, Gillian Dobbie, Joshua Zhexue Huang, Ruili Wang
Publisher	Springer Verlag
Pages	415-427
Number of pages	13
ISBN (Print)	9783319317496
DOIs	https://doi.org/10.1007/978-3-319-31750-2_33
Publication status	Published - 2016
Event	20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016 - Auckland, New Zealand Duration: 19 Apr 2016 → 22 Apr 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9652 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016
Country/Territory	New Zealand
City	Auckland
Period	19/04/16 → 22/04/16

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10.1007/978-3-319-31750-2_33

Cite this

Ranbaduge, T., Vatsalan, D., Christen, P., & Verykios, V. (2016). Hashing-based distributed multi-party blocking for privacy-preserving record linkage. In J. Bailey, L. Khan, T. Washio, G. Dobbie, J. Z. Huang, & R. Wang (Eds.), Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings (pp. 415-427). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9652 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-31750-2_33

Ranbaduge, Thilina ; Vatsalan, Dinusha ; Christen, Peter et al. / Hashing-based distributed multi-party blocking for privacy-preserving record linkage. Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. editor / James Bailey ; Latifur Khan ; Takashi Washio ; Gillian Dobbie ; Joshua Zhexue Huang ; Ruili Wang. Springer Verlag, 2016. pp. 415-427 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In many application domains organizations require information from multiple sources to be integrated. Due to privacy and confidentiality concerns often these organizations are not willing or allowed to reveal their sensitive and personal data to other database owners, and to any external party. This has led to the emerging research discipline of privacy-preserving record linkage (PPRL). We propose a novel blocking approach for multi-party PPRL to efficiently and effectively prune the record sets that are unlikely to match. Our approach allows each database owner to perform blocking independently except for the initial agreement of parameter settings and a final central hashing-based clustering. We provide an analysis of our technique in terms of complexity, quality, and privacy, and conduct an empirical study with large datasets. The results show that our approach is scalable with the size of the datasets and the number of parties, while providing better quality and privacy than previous multi-party private blocking approaches.",

keywords = "Bloom filters, Clustering, Locality sensitive hashing",

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

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016 ; Conference date: 19-04-2016 Through 22-04-2016",

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doi = "10.1007/978-3-319-31750-2_33",

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Ranbaduge, T, Vatsalan, D, Christen, P & Verykios, V 2016, Hashing-based distributed multi-party blocking for privacy-preserving record linkage. in J Bailey, L Khan, T Washio, G Dobbie, JZ Huang & R Wang (eds), Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9652 LNAI, Springer Verlag, pp. 415-427, 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, Auckland, New Zealand, 19/04/16. https://doi.org/10.1007/978-3-319-31750-2_33

Hashing-based distributed multi-party blocking for privacy-preserving record linkage. / Ranbaduge, Thilina; Vatsalan, Dinusha; Christen, Peter et al.
Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. ed. / James Bailey; Latifur Khan; Takashi Washio; Gillian Dobbie; Joshua Zhexue Huang; Ruili Wang. Springer Verlag, 2016. p. 415-427 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9652 LNAI).

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

TY - GEN

T1 - Hashing-based distributed multi-party blocking for privacy-preserving record linkage

AU - Ranbaduge, Thilina

AU - Vatsalan, Dinusha

AU - Christen, Peter

AU - Verykios, Vassilios

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - In many application domains organizations require information from multiple sources to be integrated. Due to privacy and confidentiality concerns often these organizations are not willing or allowed to reveal their sensitive and personal data to other database owners, and to any external party. This has led to the emerging research discipline of privacy-preserving record linkage (PPRL). We propose a novel blocking approach for multi-party PPRL to efficiently and effectively prune the record sets that are unlikely to match. Our approach allows each database owner to perform blocking independently except for the initial agreement of parameter settings and a final central hashing-based clustering. We provide an analysis of our technique in terms of complexity, quality, and privacy, and conduct an empirical study with large datasets. The results show that our approach is scalable with the size of the datasets and the number of parties, while providing better quality and privacy than previous multi-party private blocking approaches.

AB - In many application domains organizations require information from multiple sources to be integrated. Due to privacy and confidentiality concerns often these organizations are not willing or allowed to reveal their sensitive and personal data to other database owners, and to any external party. This has led to the emerging research discipline of privacy-preserving record linkage (PPRL). We propose a novel blocking approach for multi-party PPRL to efficiently and effectively prune the record sets that are unlikely to match. Our approach allows each database owner to perform blocking independently except for the initial agreement of parameter settings and a final central hashing-based clustering. We provide an analysis of our technique in terms of complexity, quality, and privacy, and conduct an empirical study with large datasets. The results show that our approach is scalable with the size of the datasets and the number of parties, while providing better quality and privacy than previous multi-party private blocking approaches.

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DO - 10.1007/978-3-319-31750-2_33

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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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BT - Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings

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A2 - Khan, Latifur

A2 - Washio, Takashi

A2 - Dobbie, Gillian

A2 - Huang, Joshua Zhexue

A2 - Wang, Ruili

PB - Springer Verlag

T2 - 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016

Y2 - 19 April 2016 through 22 April 2016

ER -

Ranbaduge T, Vatsalan D, Christen P, Verykios V. Hashing-based distributed multi-party blocking for privacy-preserving record linkage. In Bailey J, Khan L, Washio T, Dobbie G, Huang JZ, Wang R, editors, Advances in Knowledge Discovery and Data Mining - 20th Pacific-Asia Conference, PAKDD 2016, Proceedings. Springer Verlag. 2016. p. 415-427. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-31750-2_33

Hashing-based distributed multi-party blocking for privacy-preserving record linkage

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