This research focuses on developing privacy-preserving collaborative design methods for additive biofabrication to advance US biomanufacturing goals. Additive biofabrication, a critical aspect of biomanufacturing, holds the potential to revolutionize healthcare by producing patient-specific medical solutions, and improving treatment outcomes. However, two key challenges must be addressed to fully realize the benefits: (1) accommodating the wide variability in patient-specific requirements, resulting in an infinite spectrum of potential products, and (2) protecting sensitive patient data throughout the manufacturing workflow. Current approaches rely on iterative experiments to refine product designs, a process that is both time-consuming and resource-intensive thereby limiting diversity and scalability. This research looks to accelerate the development of biofabricated products by establishing methodologies that enable secure knowledge transfer between manufacturing facilities while preserving data privacy. The developed methodologies will be assessed on a distributed computational testbed, as well as on a laboratory-based experimental testbed that will mimic two collaborating biofabrication facilities. This project will promote interdisciplinary research at the intersection of data analytics, privacy, and advanced manufacturing. Outreach efforts include industry engagement, curriculum development, and undergraduate research opportunities. This research pursues three interc