Cloud computing is gain popularity due to its cost-effective storage and computation. There are few studies on how to leverage cloud computing resources to facilitate healthcare research in a privacy preserving manner. This project proposes an advanced framework that combines rigorous privacy protection and encryption techniques to facilitate healthcare data sharing in the cloud environment. Comparing to traditional centralized data anonymization, we are facing major challenges such as lack of global knowledge and the difficulty to enforce consistency. We adopt differential privacy as our privacy criteria and will leverage homomorphic encryption and Yao's garbled circuit protocol to build secure yet scalable information exchange to overcome the barrier.

Public Health Relevance

Sustainability and privacy are critical concerns in handling large and growing healthcare data. New challenges emerge as new paradigms like cloud computing become popular for cost-effective storage and computation. This project will develop an advanced framework to combine rigorous privacy protection and encryption techniques to facilitate healthcare data sharing in the cloud environment.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Exploratory/Developmental Grants (R21)
Project #
1R21LM012060-01
Application #
8810023
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
Project Start
2014-09-15
Project End
2016-08-31
Budget Start
2014-09-15
Budget End
2015-08-31
Support Year
1
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California San Diego
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
City
La Jolla
State
CA
Country
United States
Zip Code
92093
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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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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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