Privacy-aware Data Federation
In the project, we virtually integrate heterogeneous personal data sources to facilitate cross-domain studies - e.g., allowing researchers to access the data and perform data analysis interactively. Since personal data is very sensitive, therefore, I research and develop a module called 'privacy preservation' where I automatically detect privacy concern level of users to provide sufficient privacy protection. The state-of-the-art approach is using machine learning model to detect privacy-concern level that exists in the data itself. Based on the detected level, we can inject a sufficient amount of noise into analytic results to protect privacy. Therefore, our project would require high-performance computation hardware to perform the task.