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)
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
Project End
Budget Start
Budget End
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
Zhao, Yongan; Wang, Xiaofeng; Jiang, Xiaoqian et al. (2015) Choosing blindly but wisely: differentially private solicitation of DNA datasets for disease marker discovery. J Am Med Inform Assoc 22:100-8