Privacy-Preserving Analytic Methods in Vaccine Research: A Scoping Review of Synthetic Data and Federated Learning Approaches

This project registers a scoping review protocol that maps how privacy-preserving analytic methods (PPAMs) are used in vaccine research. Vaccine effectiveness, safety, and coverage studies increasingly depend on multi-institutional real-world data, but privacy, legal, governance, and interoperability constraints often limit sharing of individual-level information across jurisdictions. PPAMs such as federated learning, federated analytics, synthetic data generation, and privacy-preserving record linkage can support collaboration while keeping sensitive data at the source and reducing disclosure risk. The review will (1) identify empirical vaccine studies that implement PPAMs, (2) describe the vaccine types and outcome domains addressed, including effectiveness, safety, coverage or uptake, immunogenicity, and related outcomes, (3) summarize reported privacy risks and safeguards, including any stated threat models, privacy parameters when available, secure aggregation, tokenization or linkage protections, and differential privacy when used, and (4) describe how utility and validity are assessed, including benchmarking, validation, and reported privacy–utility trade-offs. The review will also capture governance and equity considerations, including implications for subgroup analyses, rare outcomes, and cross-jurisdiction collaboration. Findings will be summarized using descriptive and thematic approaches to map PPAM applications in vaccine research and to highlight gaps in transparency, validation, reproducibility, and equity-relevant implementation. The results are intended to support privacy-aware, policy-relevant vaccine research and public health data governance.