Private Distributed Analytics Platform for Health Data

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Human health depends on many complex factors, including environment, gender, diet, ethnicity and genetic variations. Therefore, large amounts of health data are needed from different sources to understand the impact of different variables on health and disease. However, due to the differences in policies and potential misuse of sensitive health data, it is hard to establish centralized data repositories among different geographical entities. As a result, raw data never leaves the boundary of an institution or a government. For example, Manitoba Centre for Health Policy (MCHP) does not disseminate raw data beyond the institution. Therefore, it is imperative to design distributed frameworks capable of performing health data analysis while protecting privacy. Federated learning, a popular framework for collaborative learning, can enable data recipients to conduct private data analysis stored across healthcare organizations. Federated learning allows multiple data providers (e.g., hospitals) to build a shared machine learning model without sharing the raw data. Each data provider runs the model on its dataset and shares only intermediate results. However, recent research shows that aggregated data (i.e., intermediate results) transmitted in the federated learning approach can still cause privacy attacks. Therefore, incorporating a privacy model to thwart such attacks, particularly for health data, is crucial in developing a private federated learning framework for health data. The long-term objective of this research program is to develop a data management system that can integrate and share sensitive data for different applications, that is usable and scalable, and that ensures the privacy and accuracy of data. The short-term objective is to develop a private data analytics framework for health data using the federated learning model. Two important privacy requirements have to be satisfied: (1) the privacy of individuals whose data are being collected must be preserved, and (2) the privacy of a data provider against other providers needs to be ensured. I will study novel methods to combine state-of-the-art cryptographic and differential privacy models to develop a private federated learning framework for health data analysis. Federated learning can overcome the limitations of the traditional centralized approach. However, the adoption of federated learning in the healthcare sector has been slower than other industries due to the sensitive nature of the data and the simultaneous need for high data utility. The outcomes of this research program will help to bridge this gap by developing a scalable, accurate, and private federated learning framework. The methods developed will also benefit related research areas (e.g., federated learning for IoT, finance, and smart transportation). In addition to publications and software distributions, this research program will also train HQPs in privacy, data science and machine learning technologies.