Optimization of the Mainzelliste software for fast privacy-preserving record linkage

Rohde, Florens; Franke, Martin; Sehili, Ziad; Lablans, Martin; Rahm, Erhard

doi:10.1186/s12967-020-02678-1

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…Mainzelliste can function as a master patient id generator, as well as a primary and secondary pseudonym management tool in combination with identifying data. It can perform local probabilistic record linkage for patient de-duplication using a highly optimized version [ 45 ] of the EpiLink [ 9 ] algorithm.…”

Section: Methodsmentioning

confidence: 99%

Record linkage based patient intersection cardinality for rare disease studies using Mainzelliste and secure multi-party computation

et al. 2022

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Background The low number of patients suffering from any given rare diseases poses a difficult problem for medical research: With the exception of some specialized biobanks and disease registries, potential study participants’ information are disjoint and distributed over many medical institutions. Whenever some of those facilities are in close proximity, a significant overlap of patients can reasonably be expected, further complicating statistical study feasibility assessments and data gathering. Due to the sensitive nature of medical records and identifying data, data transfer and joint computations are often forbidden by law or associated with prohibitive amounts of effort. To alleviate this problem and to support rare disease research, we developed the Mainzelliste Secure EpiLinker (MainSEL) record linkage framework, a secure Multi-Party Computation based application using trusted-third-party-less cryptographic protocols to perform privacy-preserving record linkage with high security guarantees. In this work, we extend MainSEL to allow the record linkage based calculation of the number of common patients between institutions. This allows privacy-preserving statistical feasibility estimations for further analyses and data consolidation. Additionally, we created easy to deploy software packages using microservice containerization and continuous deployment/continuous integration. We performed tests with medical researchers using MainSEL in real-world medical IT environments, using synthetic patient data. Results We show that MainSEL achieves practical runtimes, performing 10 000 comparisons in approximately 5 minutes. Our approach proved to be feasible in a wide range of network settings and use cases. The “lessons learned” from the real-world testing show the need to explicitly support and document the usage and deployment for both analysis pipeline integration and researcher driven ad-hoc analysis use cases, thus clarifying the wide applicability of our software. MainSEL is freely available under: https://github.com/medicalinformatics/MainSEL Conclusions MainSEL performs well in real-world settings and is a useful tool not only for rare disease research, but medical research in general. It achieves practical runtimes, improved security guarantees compared to existing solutions, and is simple to deploy in strict clinical IT environments. Based on the “lessons learned” from the real-word testing, we hope to enable a wide range of medical researchers to meet their needs and requirements using modern privacy-preserving technologies.

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Section: Methodsmentioning

confidence: 99%

Record linkage based patient intersection cardinality for rare disease studies using Mainzelliste and secure multi-party computation

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mainzelliste can function as a master patient id generator, as well as a management tool for primary and secondary pseudonyms in combination with identifying data. It can perform local probabilistic record linkage for patient de-duplication using a highly optimized version [45] of the EpiLink [9] algorithm.…”

Section: Mainzelliste Framework For Patientmentioning

confidence: 99%

Record Linkage based Patient Intersection Cardinality for Rare Disease Studies using Mainzelliste and Secure Multi-Party Computation

Kussel

Brenner

Tremper

et al. 2022

Preprint

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Background: The low number of patients of any rare diseases poses a difficult problem for medical research: apart from some specialized biobanks and disease registries, potential study participants’ information are disjoint and distributed over many medical institutions. Whenever some of those facilities are in close proximity, a significant overlap of patients can reasonably be expected, further complicating statistical study feasibility assessments and data gathering. Due to the sensitive nature of medical records and identifying data, data transfer and joint computations are often legally forbidden or associated with prohibitive amounts of effort. To alleviate this problem and to support rare disease research, we developed the Mainzelliste Secure EpiLinker (MainSEL) record linkage framework, a secure Multi-Party Computation based application using trusted-third-party-less cryptographic protocols to perform privacy-preserving record linkage with high security guarantees. In this work we extend MainSEL to allow the record linkage based calculation of the number of common patients between institutions. This allows privacy-preserving statistical feasibility estimations for further analyses and data consolidation. Additionally, we created easy to deploy software packages using microservice containerization and continuous deployment/continuous integration. We performed tests with medical researchers using MainSEL in real-world medical IT environments, using synthetic patient data. Results: We show that MainSEL achieves practical runtimes, performing 10 000 comparisons in approximately 5 minutes. While already technically feasible in a wide range of network settings and use cases, the “lessons learned” from the real-word testing show the need to explicitly support and document the usage and deployment for both analysis pipeline integration and researcher driven ad-hoc analysis use cases, thus clarifying the wide applicability of our software. MainSEL is freely available under: https://github.com/medicalinformatics/MainSEL Conclusions: MainSEL performes well in real-world settings and is a useful tool for not only rare disease research, but medical research in general. It achieves practical runtimes, improved security guarantees compared to existing solutions, and is feasible to deploy in strict clinical IT environments. Based on the “lessons learned” from the real-word testing we hope to enable a wide range of medical researchers to meet their actual needs and requirements using modern privacy-preserving technologies.

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An enhanced privacy-preserving record linkage approach for multiple databases

et al. 2022

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Optimization of the Mainzelliste software for fast privacy-preserving record linkage

Cited by 6 publications

References 23 publications

Record linkage based patient intersection cardinality for rare disease studies using Mainzelliste and secure multi-party computation

Record linkage based patient intersection cardinality for rare disease studies using Mainzelliste and secure multi-party computation

Record Linkage based Patient Intersection Cardinality for Rare Disease Studies using Mainzelliste and Secure Multi-Party Computation

An enhanced privacy-preserving record linkage approach for multiple databases

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