A sandbox study proposal for private and distributed health data analysis

TL;DR

This paper proposes a secure, distributed health data analysis platform that leverages federated analysis, secure multi-party aggregation, and differential privacy to enhance privacy while enabling clinical research.

Contribution

It introduces a novel sandbox study framework for privacy-preserving health data analysis compliant with European legislation, combining multiple advanced techniques.

Findings

Differential privacy combined with secure aggregation improves privacy-utility trade-off.

Numerical experiments validate the feasibility of the proposed methods.

The platform supports research without centralizing sensitive health data.

Abstract

This paper presents a sandbox study proposal focused on the distributed processing of personal health data within the Vinnova-funded SARDIN project. The project aims to develop the Health Data Bank (H\"alsodatabanken in Swedish), a secure platform for research and innovation that complies with the European Health Data Space (EHDS) legislation. By minimizing the sharing and storage of personal data, the platform sends analysis tasks directly to the original data locations, avoiding centralization. This approach raises questions about data controller responsibilities in distributed environments and the anonymization status of aggregated statistical results. The study explores federated analysis, secure multi-party aggregation, and differential privacy techniques, informed by real-world examples from clinical research on Parkinson's disease, stroke rehabilitation, and wound analysis. To validate the proposed study, numerical experiments were conducted using four open-source datasets to assess the feasibility and effectiveness of the proposed methods. The results support the methods for the proposed sandbox study by demonstrating that differential privacy in combination with secure aggregation techniques significantly improves the privacy-utility trade-off.