Home > Publications database > Optimization of the Mainzelliste software for fast privacy-preserving record linkage. > print

001     166796
005     20240229133530.0
024 7 _ |a 10.1186/s12967-020-02678-1
|2 doi
024 7 _ |a pmid:33451317
|2 pmid
037 _ _ |a DKFZ-2021-00126
041 _ _ |a eng
082 _ _ |a 610
100 1 _ |a Rohde, Florens
|0 0000-0001-7114-1669
|b 0
245 _ _ |a Optimization of the Mainzelliste software for fast privacy-preserving record linkage.
260 _ _ |a London
|c 2021
|b BioMed Central
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1611067943_1442
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a Data analysis for biomedical research often requires a record linkage step to identify records from multiple data sources referring to the same person. Due to the lack of unique personal identifiers across these sources, record linkage relies on the similarity of personal data such as first and last names or birth dates. However, the exchange of such identifying data with a third party, as is the case in record linkage, is generally subject to strict privacy requirements. This problem is addressed by privacy-preserving record linkage (PPRL) and pseudonymization services. Mainzelliste is an open-source record linkage and pseudonymization service used to carry out PPRL processes in real-world use cases.We evaluate the linkage quality and performance of the linkage process using several real and near-real datasets with different properties w.r.t. size and error-rate of matching records. We conduct a comparison between (plaintext) record linkage and PPRL based on encoded records (Bloom filters). Furthermore, since the Mainzelliste software offers no blocking mechanism, we extend it by phonetic blocking as well as novel blocking schemes based on locality-sensitive hashing (LSH) to improve runtime for both standard and privacy-preserving record linkage.The Mainzelliste achieves high linkage quality for PPRL using field-level Bloom filters due to the use of an error-tolerant matching algorithm that can handle variances in names, in particular missing or transposed name compounds. However, due to the absence of blocking, the runtimes are unacceptable for real use cases with larger datasets. The newly implemented blocking approaches improve runtimes by orders of magnitude while retaining high linkage quality.We conduct the first comprehensive evaluation of the record linkage facilities of the Mainzelliste software and extend it with blocking methods to improve its runtime. We observed a very high linkage quality for both plaintext as well as encoded data even in the presence of errors. The provided blocking methods provide order of magnitude improvements regarding runtime performance thus facilitating the use in research projects with large datasets and many participants.
536 _ _ |a 315 - Bildgebung und Radioonkologie (POF4-315)
|0 G:(DE-HGF)POF4-315
|c POF4-315
|x 0
|f POF IV
588 _ _ |a Dataset connected to CrossRef, PubMed,
650 _ 7 |a Blocking
|2 Other
650 _ 7 |a Locality-sensitive hashing
|2 Other
650 _ 7 |a Mainzelliste
|2 Other
650 _ 7 |a Privacy-preserving record linkage
|2 Other
700 1 _ |a Franke, Martin
|b 1
700 1 _ |a Sehili, Ziad
|b 2
700 1 _ |a Lablans, Martin
|0 P:(DE-He78)e4ad7b4e684492de43cfcb12e5397439
|b 3
|u dkfz
700 1 _ |a Rahm, Erhard
|b 4
773 _ _ |a 10.1186/s12967-020-02678-1
|g Vol. 19, no. 1, p. 33
|0 PERI:(DE-600)2118570-0
|n 1
|p 33
|t Journal of translational medicine
|v 19
|y 2021
|x 1479-5876
909 C O |o oai:inrepo02.dkfz.de:166796
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 3
|6 P:(DE-He78)e4ad7b4e684492de43cfcb12e5397439
913 0 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF3-310
|0 G:(DE-HGF)POF3-315
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-300
|4 G:(DE-HGF)POF
|v Imaging and radiooncology
|x 0
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-315
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Bildgebung und Radioonkologie
|x 0
914 1 _ |y 2021
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b J TRANSL MED : 2018
|d 2020-09-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2020-09-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-09-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2020-09-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2020-09-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2020-09-04
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Blind peer review
|d 2020-09-04
915 _ _ |a Creative Commons Attribution CC BY (No Version)
|0 LIC:(DE-HGF)CCBYNV
|2 V:(DE-HGF)
|b DOAJ
|d 2020-09-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2020-09-04
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2020-09-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2020-09-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2020-09-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2020-09-04
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2020-09-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2020-09-04
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2020-09-04
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2020-09-04
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2020-09-04
920 1 _ |0 I:(DE-He78)E260-20160331
|k E260
|l Verbundinformationssysteme
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)E260-20160331
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21