Date: November 14, 2019

Time: 10:00 am – 12:00 pm

Location: Lambton Tower Room 3105

Blockchain is a decentralized and peer-to-peer ledger technology that adds transparency, traceability, and immutability to data. It has shown a great promise to mitigate the interoperability problem and privacy concerns in the de facto electronic health record management systems and has recently received increasing attention from the healthcare industry. Several blockchain-based and decentralized health data management mechanisms have been proposed to improve the quality of care delivery to patients. Apart from care delivery, health data has other important usages, such as education, regulation, research, public health improvement, and policy support. However, existing privacy acts prohibit health institutions and providers from sharing patients’ data with third parties. Therefore, research institutions that conduct research on private health data need a secure system that provides accurate analysis results while preserving patient privacy and minimizing the risks of data breaches. In this thesis, we propose a novel privacy-preserving method for statistical analysis of health data. We leveraged the blockchain technology and Paillier encryption algorithm to increase the accuracy of data analysis while preserving the privacy of patients. Smart contracts were used to carry out mathematical operations on the encrypted records in a secure manner. We were able to successfully deploy the proposed scheme on Hyperledger Fabric, a permissioned and consortium blockchain platform. Compared to the previous works, the proposed model enjoys the benefits of a distributed blockchain-based environment, which include higher availability and enhanced data security. The experimental results show the feasibility of this method with a reasonable amount of time for regular queries.

Internal Reader: Dr. Jianguo Lu

External Reader: Dr. Myron Hlynka

Advisor: Dr. Saeed Samet

Chair: Dr. Sherif Saad

 

5113 Lambton Tower, 401 Sunset Ave., Windsor ON., N9B 3P4 (519) 253-3000 Ext. 3716 csgradinfo@uwindsor.ca