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von Maltitz M, Ballhausen H, Kaul D, et al. A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation. JMIR Med Inform. 2021;9(1):e22158doi: 10.2196/22158.
von Maltitz, M., Ballhausen, H., Kaul, D., Fleischmann, D. F., Niyazi, M., Belka, C., & Carle, G. (2021). A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation. JMIR medical informatics, 9(1), e22158. https://doi.org/10.2196/22158
von Maltitz, Marcel, et al. "A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation." JMIR medical informatics vol. 9,1 (2021): e22158. doi: https://doi.org/10.2196/22158
von Maltitz M, Ballhausen H, Kaul D, Fleischmann DF, Niyazi M, Belka C, Carle G. A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation. JMIR Med Inform. 2021 Jan 18;9(1):e22158. doi: 10.2196/22158. PMID: 33459602; PMCID: PMC7850908.
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JMIR Med Inform. 2021 Jan 18;9(1):e22158. doi: 10.2196/22158.

A Privacy-Preserving Log-Rank Test for the Kaplan-Meier Estimator With Secure Multiparty Computation: Algorithm Development and Validation.

JMIR medical informatics

Marcel von Maltitz, Hendrik Ballhausen, David Kaul, Daniel F Fleischmann, Maximilian Niyazi, Claus Belka, Georg Carle

Affiliations

  1. Chair of Network Architectures and Services, Department of Informatics, Technical University of Munich, TUM, Garching, Germany.
  2. Department of Radiation Oncology, University Hospital, Ludwig-Maximilians-Universität München, LMU, Munich, Germany.
  3. German Cancer Consortium (DKTK), partner site Munich, Munich, Germany.
  4. Department of Radiation Oncology, Charité - University Medicine, Berlin, Germany.
  5. German Cancer Consortium (DKTK), partner site Berlin, Berlin, Germany.
  6. German Cancer Research Center (DKFZ), Heidelberg, Germany.

PMID: 33459602 PMCID: PMC7850908 DOI: 10.2196/22158

Abstract

BACKGROUND: Patient data is considered particularly sensitive personal data. Privacy regulations strictly govern the use of patient data and restrict their exchange. However, medical research can benefit from multicentric studies in which patient data from different institutions are pooled and evaluated together. Thus, the goals of data utilization and data protection are in conflict. Secure multiparty computation (SMPC) solves this conflict because it allows direct computation on distributed proprietary data-held by different data owners-in a secure way without exchanging private data.

OBJECTIVE: The objective of this work was to provide a proof-of-principle of secure and privacy-preserving multicentric computation by SMPC with real-patient data over the free internet. A privacy-preserving log-rank test for the Kaplan-Meier estimator was implemented and tested in both an experimental setting and a real-world setting between two university hospitals.

METHODS: The domain of survival analysis is particularly relevant in clinical research. For the Kaplan-Meier estimator, we provided a secure version of the log-rank test. It was based on the SMPC realization SPDZ and implemented via the FRESCO framework in Java. The complexity of the algorithm was explored both for synthetic data and for real-patient data in a proof-of-principle over the internet between two clinical institutions located in Munich and Berlin, Germany.

RESULTS: We obtained a functional realization of an SMPC-based log-rank evaluation. This implementation was assessed with respect to performance and scaling behavior. We showed that network latency strongly influences execution time of our solution. Furthermore, we identified a lower bound of 2 Mbit/s for the transmission rate that has to be fulfilled for unimpeded communication. In contrast, performance of the participating parties have comparatively low influence on execution speed, since the peer-side processing is parallelized and the computational time only constitutes 30% to 50% even with optimal network settings. In the real-world setting, our computation between three parties over the internet, processing 100 items each, took approximately 20 minutes.

CONCLUSIONS: We showed that SMPC is applicable in the medical domain. A secure version of commonly used evaluation methods for clinical studies is possible with current implementations of SMPC. Furthermore, we infer that its application is practically feasible in terms of execution time.

©Marcel von Maltitz, Hendrik Ballhausen, David Kaul, Daniel F Fleischmann, Maximilian Niyazi, Claus Belka, Georg Carle. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 18.01.2021.

Keywords: cryptography; data protection; multicentric studies; privacy; privacy preservation; secure multiparty computation

References

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