There is increasing concern in disclosing sensitive information when clinical data are disseminated, given the potential for breach of individual privacy. Data sharing has become critical in the acceleration of biomedical research and healthcare quality improvement. We will develop new methods for privacy protection that can adapt to the amount of the data being disseminated and the sensitivity of certain variables.
Our first aim i s to measure fine-grained privacy risk of individual records in patient sub- populations This index can be used to monitor and customize privacy protection of individual clinical records and help prioritize efforts in privacy protection.
The second aim i s to develop a new and practical method to support privacy-preserving data dissemination in both centralized and distributed environments, with or without knowledge of which analytic techniques will be applied to the disclosed data.
The third aim i s to speed up privacy preserving algorithms through advanced parallelization techniques. If successful, these new methods will allow privacy protection for large data set dissemination/analysis in real time.
These aims are faithful to the mission of the National Library of Medicine, and they are tightly related to the mentors'efforts i leading the development of trustworthy data sharing and individualized predictive models as part of the National Center for Biomedical Computing (NCBC), iDASH (integrating Data for analysis, Anonymization, and SHaring). The applicant wishes to use this funding opportunity to complement his computer science skills with biomedical knowledge, and specialized training in parallel computing to investigate new algorithms for privacy protection in disseminated data. Success in this project will lead to his long-term goal of becoming an independently funded investigator and joining the core faculty of the Division of Biomedical Informatics at UCSD.
There are important tradeoffs between disseminating clinical and genetic data for societal benefits and protecting personal privacy. We will develop practical solutions to address fine-grained privacy and usability trade-offs, provide multi-resolution protection to satisfy needs of different stakeholders, and accelerate privacy-preserving algorithms to support efficient data anonymization, analysis, and sharing.
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|Wang, Shuang; Zhang, Yuchen; Dai, Wenrui et al. (2016) HEALER: homomorphic computation of ExAct Logistic rEgRession for secure rare disease variants analysis in GWAS. Bioinformatics 32:211-8|
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|Huh, Jina; Marmor, Rebecca; Jiang, Xiaoqian (2016) Lessons Learned for Online Health Community Moderator Roles: A Mixed-Methods Study of Moderators Resigning From WebMD Communities. J Med Internet Res 18:e247|
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|Lin, Shengda; Yin, Yi A; Jiang, Xiaoqian et al. (2016) Multi-OMICs and Genome Editing Perspectives on Liver Cancer Signaling Networks. Biomed Res Int 2016:6186281|
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|Shi, Haoyi; Jiang, Chao; Dai, Wenrui et al. (2016) Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE). BMC Med Inform Decis Mak 16 Suppl 3:89|
|Yang, Lei; Wang, Shuang; Jiang, Xiaoqian et al. (2016) PATTERN: Pain Assessment for paTients who can't TEll using Restricted Boltzmann machiNe. BMC Med Inform Decis Mak 16 Suppl 3:73|
|Dulai, Parambir S; Singh, Siddharth; Jiang, Xiaoqian et al. (2016) The Real-World Effectiveness and Safety of Vedolizumab for Moderate-Severe Crohn's Disease: Results From the US VICTORY Consortium. Am J Gastroenterol 111:1147-55|
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