Healthcare data often contains an enormous amount of hidden information that can only be extracted after a sufficiently large amount of them is aggregated. However, since healthcare data are usually generated in a distributed manner and stored at different sites such as hospitals, pharmacies, and biomedical labs, it is extremely difficult to aggregate and share them due to privacy concerns. Thus, it is urgently needed to develop effective techniques to gather biomedical data and, meanwhile, protect their privacy. Besides privacy, there is growing
concern regarding ML algorithms for healthcare to inflict unfairness and bias. To provide much more sound solutions to these problems, we propose to develop a set of differentially private and fair learning algorithms for healthcare, which can be used as algorithmic tools to both enhance the data-sharing ability and mitigate bias in healthcare systems. Specifically, we are concerned about the challenges from healthcare data, complex tasks in healthcare, and distributed environment.
concern regarding ML algorithms for healthcare to inflict unfairness and bias. To provide much more sound solutions to these problems, we propose to develop a set of differentially private and fair learning algorithms for healthcare, which can be used as algorithmic tools to both enhance the data-sharing ability and mitigate bias in healthcare systems. Specifically, we are concerned about the challenges from healthcare data, complex tasks in healthcare, and distributed environment.