FEDERATED ANALYTICS AND DIFFERENTIAL PRIVACY FOR SECURE CLINICAL TRIAL DATA PROCESSING ACROSS JURISDICTIONAL BOUNDARIES

Abstract

With the increasing global scale of clinical trials, sharing sensitive patient data across borders poses critical challenges due to regulatory differences and privacy concerns. This paper explores a novel framework integrating Federated Analytics (FA) with Differential Privacy (DP) to enable secure, privacy-preserving analysis of clinical trial data across jurisdictional boundaries. We propose a hybrid model leveraging local computation, encrypted aggregation, and noise-based privacy guarantees to maintain compliance with regulations such as HIPAA and GDPR. Our prototype evaluation across three international healthcare institutions demonstrated preserved data utility (±3.2% deviation) while achieving robust privacy metrics. This approach addresses the demand for cross-border clinical data integration without compromising confidentiality or ethical standards.