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Decentralized Differentially-Private Methods for Dynamic Data Release and Analysis (DECENTRALIZED)

Project Description

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.

Principal Investigator (PI): Xiaoqian Jiang

Co-PIs: ​Lucila Ohno-Machado

Trainees: Junghye Lee

Grant: ​R01GM118609

Contact: ​x1jiang@ucsd.edu

Start Date: ​1/01/17

Expected Duration: 12/31/20

NIH Project Information