Secure and Private Collaborative Environments for Biomedical Analytics Privacy and confidentiality are critical to healthcare. However, preserving privacy is a non-trivial task because any protection scheme essentially involves a tradeoff with data utility. Furthermore, lack of access to biomedical data can lead to fragmentation of care, resulting in higher economic and social costs, higher safety risk from avoidable drug interactions side-effects, and is a significant impediment to biomedical research. The primary aim of this project is to facilitate biomedical research in collaborative environments by developing technologies for secure and privacy-preserving exploratory analysis. This is critical to the big data to knowledge (BD2K) initiative since it can facilitate biomedical research in collaborative environments. The proposed work addresses two complementary aims. First, we will develop technologies that enable exploratory analysis of data to determine its usability and relevance to the specific biomedical research task. We will also develop technologies to enable the measurement and mitigation of additional privacy/security risk due to accessing this data, thus enabling proper control over the data. The proposed solutions will utilize and enhance the state of the art technological solutions such as privacy-preserving sampling and analysis algorithms, risk-based access control, and query auditing. By doing so, the proposed work will enhance biomedical study design, exploratory data analysis, and hypothesis discovery without compromising on privacy. The project will result in open-source, freely available software tools to perform utility analysis and risk analysis. These will be integrated into REDCap, a data collection and management system used widely for providing translational research informatics support. Several ongoing projects at Rutgers and UCSD will serve as initial pilot customers for the proposed work, with other research groups also being involved at a later stage.
Statement of Relevance to Public Health Being able to securely access data within collaborative environments and to be able to do exploratory analytics in a privacy-preserving way is critical for public health research and monitoring of chronic diseases. Lack of access to healthcare data can lead to fragmentation of care, resulting in higher economic and social costs, higher safety risk from avoidable drug interactions side-effects, and lower compliance with treatment best practices. The proposed work will facilitate creation and testing of new diagnostic, tracking and outcome measures amenable to better analysis and the development of tools for enhanced study design, management and analysis without compromising on privacy. In addition, the proposed work will contribute to satisfying: (1) public health researchers' need to access integrated data in order to identify patterns of fragmentation, and propose interventions to remediate those problems; (2) agencies' need for patient level details to specifically target such public health interventions to the patients with the most fragmented and suboptimal care.
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| Bonomi, Luca; Jiang, Xiaoqian (2018) Patient ranking with temporally annotated data. J Biomed Inform 78:43-53 |
| Kim, Miran; Song, Yongsoo; Wang, Shuang et al. (2018) Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation. JMIR Med Inform 6:e19 |
| Sadat, Md Nazmus; Jiang, Xiaoqian; Aziz, Md Momin Al et al. (2018) Secure and Efficient Regression Analysis Using a Hybrid Cryptographic Framework: Development and Evaluation. JMIR Med Inform 6:e14 |
| Wang, Meng; Ji, Zhanglong; Kim, Hyeon-Eui et al. (2018) Selecting Optimal Subset to release under Differentially Private M-estimators from Hybrid Datasets. IEEE Trans Knowl Data Eng 30:573-584 |
| Bonomi, Luca; Jiang, Xiaoqian (2018) Linking temporal medical records using non-protected health information data. Stat Methods Med Res 27:3304-3324 |
| Chen, Feng; Jiang, Xiaoqian; Wang, Shuang et al. (2018) Perfectly Secure and Efficient Two-Party Electronic-Health-Record Linkage. IEEE Internet Comput 22:32-41 |
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| Wang, Meng; Ji, Zhanglong; Wang, Shuang et al. (2017) Mechanisms to protect the privacy of families when using the transmission disequilibrium test in genome-wide association studies. Bioinformatics 33:3716-3725 |
| Chen, Feng; Wang, Chenghong; Dai, Wenrui et al. (2017) PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension. BMC Med Genomics 10:48 |
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