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Secure Multi-Party Computation Based Distributed Feasibility Queries - A HiGHmed Use Case

Wettstein, Reto ; Kussel, Tobias ; Hund, Hauke ; Fegeler, Christian ; Dugas, Martin ; Hamacher, Kay (2022)
Secure Multi-Party Computation Based Distributed Feasibility Queries - A HiGHmed Use Case.
In: Studies in health technology and informatics, 296
doi: 10.3233/shti220802
Article, Bibliographie

Abstract

The integration of routine medical care data into research endeavors promises great value. However, access to this extra-domain data is constrained by numerous technical and legal requirements. The German Medical Informatics Initiative (MII) - initiated by the Federal Ministry of Research and Education (BMBF) - is making progress in setting up Medical Data Integration Centers to consolidate data stored in clinical primary information systems. Unfortunately, for many research questions cross-organizational data sources are required, as one organization's data is insufficient, especially in rare disease research. A first step, for research projects exploring possible multi-centric study designs, is to perform a feasibility query, i.e., a cohort size calculation transcending organizational boundaries. Existing solutions for this problem, like the previously introduced feasibility process for the MII's HiGHmed consortium, perform well for most use cases. However, there exist use cases where neither centralized data repositories, nor Trusted Third Parties are acceptable for data aggregation. Based on open standards, such as BPMN 2.0 and HL7 FHIR R4, as well as the cryptographic techniques of secure Multi-Party Computation, we introduce a fully automated, decentral feasibility query process without any central component or Trusted Third Party. The open source implementation of the proposed solution is intended as a plugin process to the HiGHmed Data Sharing Framework. The process's concept and underlying algorithms can also be used independently.

Item Type: Article
Erschienen: 2022
Creators: Wettstein, Reto ; Kussel, Tobias ; Hund, Hauke ; Fegeler, Christian ; Dugas, Martin ; Hamacher, Kay
Type of entry: Bibliographie
Title: Secure Multi-Party Computation Based Distributed Feasibility Queries - A HiGHmed Use Case
Language: English
Date: 17 August 2022
Journal or Publication Title: Studies in health technology and informatics
Volume of the journal: 296
DOI: 10.3233/shti220802
Abstract:

The integration of routine medical care data into research endeavors promises great value. However, access to this extra-domain data is constrained by numerous technical and legal requirements. The German Medical Informatics Initiative (MII) - initiated by the Federal Ministry of Research and Education (BMBF) - is making progress in setting up Medical Data Integration Centers to consolidate data stored in clinical primary information systems. Unfortunately, for many research questions cross-organizational data sources are required, as one organization's data is insufficient, especially in rare disease research. A first step, for research projects exploring possible multi-centric study designs, is to perform a feasibility query, i.e., a cohort size calculation transcending organizational boundaries. Existing solutions for this problem, like the previously introduced feasibility process for the MII's HiGHmed consortium, perform well for most use cases. However, there exist use cases where neither centralized data repositories, nor Trusted Third Parties are acceptable for data aggregation. Based on open standards, such as BPMN 2.0 and HL7 FHIR R4, as well as the cryptographic techniques of secure Multi-Party Computation, we introduce a fully automated, decentral feasibility query process without any central component or Trusted Third Party. The open source implementation of the proposed solution is intended as a plugin process to the HiGHmed Data Sharing Framework. The process's concept and underlying algorithms can also be used independently.

Identification Number: pmid:36073487
Divisions: 10 Department of Biology
10 Department of Biology > Computational Biology and Simulation
Date Deposited: 13 Sep 2022 06:32
Last Modified: 13 Sep 2022 06:33
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