Data sharing and information exchange are playing critical roles in biomedical data science to improve quality of care, accelerate discovery, and promote meaningful secondary use of clinical data. But privacy is a big concern to the public. Existing distributed data analysis methods do not address the security and privacy issues in exchanging intermediary statistics and they cannot handle dynamic database updates very well. This project aims at designing and implementing differentially-private decentralized methods for dynamic data dissemination and analysis. We plan to use genomic and clinical data from both public domain and local institutions (UCSD and Emory) to carefully evaluate the feasibility and efficiency of our proposed new methods.
A big challenge in biomedical information sharing is to maintain privacy, as inappropriate data handling can put patient's and their family members' sensitive personal information at risk. We will develop a privacy-preserving decentralized framework for dynamic data dissemination and analysis to support cross-institutional collaboration.
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| Kim, Miran; Song, Yongsoo; Wang, Shuang et al. (2018) Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation. JMIR Med Inform 6:e19 |
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| Bonomi, Luca; Jiang, Xiaoqian (2018) Linking temporal medical records using non-protected health information data. Stat Methods Med Res 27:3304-3324 |
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| Ghasemi, Reza; Al Aziz, Md Momin; Mohammed, Noman et al. (2017) Private and Efficient Query Processing on Outsourced Genomic Databases. IEEE J Biomed Health Inform 21:1466-1472 |
| 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 |
| Kim, Yejin; Sun, Jimeng; Yu, Hwanjo et al. (2017) Federated Tensor Factorization for Computational Phenotyping. KDD 2017:887-895 |
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