SCH: Enabling Data Outsourcing and Sharing for AI-powered Parkinson's Research

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Artificial intelligence holds the promise of transforming data-driven biomedical research and computational health informatics for more accurate diagnosis and better treatment at lower cost. In the meantime, modern digital and mobile technologies make it much easier to collect information from patients in large scale. While “big” medical data offers unprecedented opportunities of building deep-learning artificial neural network (ANN) models to advance the research of complex diseases such as Parkinson’s disease (PD), it also presents unique challenges to patient data privacy. The task of training and continuously refining ANN models with data from tens of thousands of patients, each with numerous attributes and images, is computation-intensive and time-consuming. Outsourcing such computation and its data to the cloud is a viable solution. However, the problem of performing the ANN learning operations in the cloud, without the risk of leaking any patient data from their distributed sources, remains open to date. This application proposes to develop novel data masking technologies based on randomized orthogonal transformation to enable AI-computation outsourcing and data sharing, with the following two specific aims: 1) Perform two experimental studies of training ANN models with data masking in the HiperGator cloud for PD prediction and Parkinsonism diagnosis; 2) establish the theoretical foundation on data privacy, inference accuracy, and training performance of the ANN models used in the experimental studies. The interdisciplinary project team combines the expertise from data privacy, biomedical informatics, machine learning, and cloud computing to develop data outsourcing and sharing technologies for AI-powered PD research. The proposed research will remove a major roadblock that restricts medical data accessibility and hinders cloud-based operations of deep-learning artificial neural networks for biomedical research. The outcome is expected to have a broader impact beyond PD research in advancing the theory and implementation of cloud-based medical studies with data privacy protection.