Pereira I. Learning without Sharing the Data: An Evidence Map of Privacy-Preserving Collaboration in Pharmaceutical Research Download PDF

Pharmaceutical research increasingly depends on evidence distributed across companies, healthcare institutions, research networks, and jurisdictions, yet legal restrictions, commercial sensitivity, technical incompatibility, and patient-privacy obligations often prevent centralized data pooling. Privacy-preserving collaboration has therefore emerged as a possible means of learning across organizational boundaries while retaining data at their sources or limiting disclosure during analysis. However, the evidence base combines conceptually distinct technologies, heterogeneous validation designs, and inconsistent claims about privacy, analytical utility, and implementation readiness. This evidence-mapping review critically organizes recent peer-reviewed scholarship on federated learning, secure computation, synthetic data, and distributed analytics across pharmaceutical discovery, development, and safety. The review maps evidence according to the protected asset, exchanged artifact, application context, threat model, validation setting, implementation maturity, utility limitation, and translational boundary. The synthesis indicates that data locality can enable collaboration but does not itself constitute a complete privacy guarantee. Formal privacy mechanisms provide stronger protection for specified adversarial conditions, although their usefulness remains conditional on computational feasibility, privacy parameters, model performance, and organizational infrastructure. Evidence is most developed for retrospective modeling, molecular-property prediction, distributed clinical analysis, and cross-company quantitative structure–activity relationship learning. By contrast, prospective decision impact, independent security testing, semantic interoperability, governance evaluation, and routine pharmaceutical integration remain comparatively underdeveloped. The article proposes a bounded maturity interpretation that separates conceptual plausibility, technical feasibility, retrospective validation, operational piloting, and decision-integrated readiness. This synthesis is an organizing framework rather than a validated scoring instrument. Trustworthy pharmaceutical collaboration will require privacy claims to be linked explicitly to threat models, utility assessments, data suitability, reproducibility, governance, and the scientific consequences of model use.