Borges, RafaelFerreira, BrunoAntunes, Carlos MachadoMaximiano, MarisaGomes, RicardoTávora, VitorDias, ManuelBezerra, Ricardo CorreiaDomingues, PatrícioAntunes, Carlos Machado2026-01-202026-01-202025-12-24Borges, R., Ferreira, B., Antunes, C. M., Maximiano, M., Gomes, R., Távora, V., Dias, M., Bezerra, R. C., & Domingues, P. (2026). Using Secure Multi-Party Computation to Create Clinical Trial Cohorts. Journal of Cybersecurity and Privacy, 6(1), 2. https://doi.org/10.3390/jcp60100022624-800Xhttp://hdl.handle.net/10400.8/15420Article number 2.This article belongs to the Special Issue Cyber Security and Digital Forensics—2nd Edition.Acknowledgments: This work is included in the Blockchain.pt project, part of Portugal’s Recovery and Resilience Plan with the objective of spreading blockchain to various sectors and it was done in partnership with BioGHP (https://www.bioghp.com/, accessed on 25 July 2025).The increasing volume of digital medical data offers substantial research opportunities, though its complete utilization is hindered by ongoing privacy and security obstacles. This proof-of-concept study explores and confirms the viability of using Secure Multi-Party Computation (SMPC) to ensure protection and integrity of sensitive patient data, allowing the construction of clinical trial cohorts. Our findings reveal that SMPC facilitates collaborative data analysis on distributed, private datasets with negligible computational costs and optimized data partition sizes. The established architecture incorporates patient information via a blockchain-based decentralized healthcare platform and employs the MPyC library in Python for secure computations on Fast Healthcare Interoperability Resources (FHIR)-format data. The outcomes affirm SMPC’s capacity to maintain patient privacy during cohort formation, with minimal overhead. It illustrates the potential of SMPC-based methodologies to expand access to medical research data. A key contribution of this work is eliminating the need for complex cryptographic key management while maintaining patient privacy, illustrating the potential of SMPC-based methodologies to expand access to medical research data by reducing implementation barriers.engSecure Multi-Party ComputationPrivacyElectronic Health RecordsPrivate data sharingHealthcarejournal article10.3390/jcp6010002