TY - JOUR T1 - Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption JF - bioRxiv DO - 10.1101/2021.02.24.432489 SP - 2021.02.24.432489 AU - David Froelicher AU - Juan R. Troncoso-Pastoriza AU - Jean Louis Raisaro AU - Michel A. Cuendet AU - Joao Sa Sousa AU - Jacques Fellay AU - Jean-Pierre Hubaux Y1 - 2021/01/01 UR - http://biorxiv.org/content/early/2021/02/25/2021.02.24.432489.abstract N2 - In biomedical research, real-world evidence, which is emerging as an indispensable complement of clinical trials, relies on access to large quantities of patient data that typically reside at separate healthcare institutions. Conventional approaches for centralizing those data are often not feasible due to privacy and security requirements. As a result, more privacy-friendly solutions based on federated analytics are emerging. They enable to simultaneously analyse medical data distributed across a group of connected institutions. However, these techniques do not inherently protect patients’ privacy as they require institutions to share intermediate results that can reveal patient-level information. To address this issue, state-of-the-art solutions use additional privacy-preserving measures based on data obfuscation, which often introduce noise in the computation of the final result that can become too inaccurate for precision medicine use cases. We propose FAMHE, a modular system based on multiparty homomorphic encryption, that enables the privacy-preserving execution of federated analytics workflows yielding exact results and without leaking any intermediate information. To demonstrate the maturity of our approach, we reproduce the results of two published state-of-the-art centralized biomedical studies, and we demonstrate that FAMHE enables the efficient, privacy-preserving and decentralized execution of analyses that range from low computational complexity, such as Kaplan-Meier overall survival curves used in oncology, to high computational complexity, such as genome-wide association studies on millions of variants.Competing Interest StatementThe authors have declared no competing interest. ER -