1 research outputs found
Privacy-preserving dataset combination and Lasso regression for healthcare predictions
Background: Recent developments in machine learning have shown its potential impact for clinical use such as risk
prediction, prognosis, and treatment selection. However, relevant data are often scattered across different stakeholders and their use is regulated, e.g. by GDPR or HIPAA.
As a concrete use-case, hospital Erasmus MC and health insurance company Achmea have data on individuals in the
city of Rotterdam, which would in theory enable them to train a regression model in order to identify high-impact
lifestyle factors for heart failure. However, privacy and confdentiality concerns make it unfeasible to exchange these
data.
Methods: This article describes a solution where vertically-partitioned synthetic data of Achmea and of Erasmus MC
are combined using Secure Multi-Party Computation. First, a secure inner join protocol takes place to securely determine the identifiers of the patients that are represented in both datasets. Then, a secure Lasso Regression model is
trained on the securely combined data. The involved parties thus obtain the prediction model but no further information on the input data of the other parties.
Results: We implement our secure solution and describe its performance and scalability: we can train a prediction
model on two datasets with 5000 records each and a total of 30 features in less than one hour, with a minimal difference from the results of standard (non-secure) methods.
Conclusions: This article shows that it is possible to combine datasets and train a Lasso regression model on this
combination in a secure way. Such a solution thus further expands the potential of privacy-preserving data analysis in
the medical domain