Accurate training of the Cox proportional hazards model on vertically-partitioned data while preserving privacy.

ABSTRACT:

Background

Analysing distributed medical data is challenging because of data sensitivity and various regulations to access and combine data. Some privacy-preserving methods are known for analyzing horizontally-partitioned data, where different organisations have similar data on disjoint sets of people. Technically more challenging is the case of vertically-partitioned data, dealing with data on overlapping sets of people. We use an emerging technology based on cryptographic techniques called secure multi-party computation (MPC), and apply it to perform privacy-preserving survival analysis on vertically-distributed data by means of the Cox proportional hazards (CPH) model. Both MPC and CPH are explained.

Methods

We use a Newton-Raphson solver to securely train the CPH model w

SUBMITTER: Kamphorst B 

PROVIDER: S-EPMC8867891 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature