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

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 priv

SUBMITTER: von Maltitz M 

PROVIDER: S-EPMC7850908 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature