Privacy-aware Collaborative Design: Towards Mass Personalization in Additive Biofabrication

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 interconnected objectives to develop the privacy-aware collaborative design framework for additive biofabrication. First, latent encoding mechanisms and a novel single-view fusion framework will be developed to integrate design data from multiple manufacturing facilities with heterogeneous capabilities. Second, differential privacy mechanisms will be incorporated to ensure strong privacy guarantees, preventing unintended leaks of design parameters during collaboration. Third, two testbeds will be established: a numerical testbed to evaluate scalability under varying privacy budgets, computational constraints, and temporal dynamics, and an experimental testbed to validate effectiveness in fabricating patient-specific bioengineered products. Overall, successful development of privacy-aware collaborative design framework looks to reduce existing barriers in design collaboration, advancing US biomanufacturing goals by reducing product development time and increasing the wide range of biofabricated products. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.