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 #
5R21LM012060-02
Application #
8925916
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
Project Start
2014-09-15
Project End
2017-08-31
Budget Start
2015-09-01
Budget End
2017-08-31
Support Year
2
Fiscal Year
2015
Total Cost
Indirect Cost
Name
University of California San Diego
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
804355790
City
La Jolla
State
CA
Country
United States
Zip Code
92093
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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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Marmor, Rebecca A; Dai, Wenrui; Jiang, Xiaoqian et al. (2017) Increase in contralateral prophylactic mastectomy conversation online unrelated to decision-making. J Surg Res 218:253-260
Chen, Feng; Wang, Shuang; Jiang, Xiaoqian et al. (2017) PRINCESS: Privacy-protecting Rare disease International Network Collaboration via Encryption through Software guard extensionS. Bioinformatics 33:871-878

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