Recent studies and advisory reports to the government have pointed out that information sharing with appropriate privacy protection to enable health research is one of the most critical challenges of our time. Current de-identification approaches or microdata (i.e. original records) release are subject to various re-identification and disclosure risks and do not provide sufficient protection for our patients. A complementary approach is to release statistical macrodata (i.e. derived statistics), which can also be used to construct synthetic data that mimic the original data. Differential privacy has emerged in recent years as one of the strongest provable privacy guarantees for statistical data release. However, it remains a challenge to efficiently and effectively release a dataset (consistent with a set of statistics) that ensures differential privacy while guaranteeing data utiity for targeted applications. Applying differential privacy to health data presents additional new challenges due to the high dimensionality, high correlation, and cross- institution distribution in health datasets that support cross-sectional, longitudinal, and cross-institutional studies. The absence of practical data sharing software with rigorous privacy guarantees has made data providers uncomfortable with sharing data. Lack of datasets has severely hindered medical and informatics research in general. We propose to collaborate with the NCBC iDASH (integrating Data for Analysis, Anonymization, and Sharing) center to develop a Statistical Health informAtion RElease (SHARE) toolkit with differential privacy.
The specific aims are: 1) develop and evaluate novel methods for releasing statistical health data with differential privacy to address the high-dimensionality, self-correlation, and cross-institution distribution of data, 2) ue the SHARE toolkit for clinical dataset construction and use case studies using Emory Analytical Information Warehouse (AIW) and UCSD Clinical Data Warehouse for Research (CDWR) and demonstrate its utility for cohort discovery queries and hospital readmission study, and 3) deploy the SHARE toolkit at Emory and iDASH as well as Atlanta Clinical & Translational Science Institute (ACTSI) and UCSD Clinical and Translational Research Institute (CTRI). The techniques and software tools envisioned by SHARE will facilitate health information sharing for health research and have a direct impact on predictive health and translational medicine as well as informatics practice.
Collaborating with iDASH, a National Center for Biomedical Computing for 'integrating Data for Analysis, Anonymization and Sharing', we will develop innovative and practical techniques and software for protecting the privacy of human subjects in the large-scale analysis of clinical and public health data. The techniques and software tools will significantly help overcome the barrier to data access, and ultimately accelerate the public health research.
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