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

Thilina Ranbaduge; Dinusha Vatsalan; Peter Christen; Vassilios S. 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 S. Verykios

^*Corresponding author for this work

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

13 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
Subtitle of host publication	20th Pacific-Asia Conference, PAKDD 2016 Auckland, New Zealand, April 19–22, 2016 Proceedings, Part II
Editors	James Bailey, Latifur Khan, Takashi Washio, Gillian Dobbie, Joshua Zhexue Huang, Ruili Wang
Place of Publication	Cham, Switzerland
Publisher	Springer, Springer Nature
Pages	415-427
Number of pages	13
ISBN (Electronic)	9783319317502
ISBN (Print)	9783319317496
DOIs	https://doi.org/10.1007/978-3-319-31750-2_33
Publication status	Published - 2016
Externally published	Yes
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

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

Keywords

Locality sensitive hashing
Clustering
Bloom filters

Access to Document

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

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Ranbaduge, T., Vatsalan, D., Christen, P., & Verykios, V. S. (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 Auckland, New Zealand, April 19–22, 2016 Proceedings, Part II (pp. 415-427). Springer, Springer Nature. 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 Auckland, New Zealand, April 19–22, 2016 Proceedings, Part II. editor / James Bailey ; Latifur Khan ; Takashi Washio ; Gillian Dobbie ; Joshua Zhexue Huang ; Ruili Wang. Cham, Switzerland : Springer, Springer Nature, 2016. pp. 415-427

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title = "Hashing-based distributed multi-party blocking for privacy-preserving record linkage",

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

author = "Thilina Ranbaduge and Dinusha Vatsalan and Peter Christen and Verykios, {Vassilios S.}",

year = "2016",

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

language = "English",

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editor = "James Bailey and Latifur Khan and Takashi Washio and Gillian Dobbie and Huang, {Joshua Zhexue} and Ruili Wang",

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Ranbaduge, T, Vatsalan, D, Christen, P & Verykios, VS 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 Auckland, New Zealand, April 19–22, 2016 Proceedings, Part II. Springer, Springer Nature, Cham, Switzerland, 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 Auckland, New Zealand, April 19–22, 2016 Proceedings, Part II. ed. / James Bailey; Latifur Khan; Takashi Washio; Gillian Dobbie; Joshua Zhexue Huang; Ruili Wang. Cham, Switzerland: Springer, Springer Nature, 2016. p. 415-427.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding 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 S.

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.

KW - Locality sensitive hashing

KW - Clustering

KW - Bloom filters

U2 - 10.1007/978-3-319-31750-2_33

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

M3 - Conference proceeding contribution

SN - 9783319317496

SP - 415

EP - 427

BT - Advances in Knowledge Discovery and Data Mining

A2 - Bailey, James

A2 - Khan, Latifur

A2 - Washio, Takashi

A2 - Dobbie, Gillian

A2 - Huang, Joshua Zhexue

A2 - Wang, Ruili

PB - Springer, Springer Nature

CY - Cham, Switzerland

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 VS. 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 Auckland, New Zealand, April 19–22, 2016 Proceedings, Part II. Cham, Switzerland: Springer, Springer Nature. 2016. p. 415-427 doi: 10.1007/978-3-319-31750-2_33

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

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Keywords

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