MSc Thesis Defense Announcement by Mahdi Ghadamyari:"Privacy-Preserving Statistical Analysis of Health Data Using Paillier Homomorphic Encryption and Permissioned Blockchain"

Thursday, November 14, 2019 - 10:00 to 12:00



The School of Computer Science is pleased to present…


MSc Thesis Defense by:  Mahdi Ghadamyari

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.


Thesis Committee:

Internal Reader: Dr. Jianguo Lu
External Reader: Dr. Myron Hlynka
Advisor: Dr. Saeed Samet
Chair: Dr. Sherif Saad


MSc Thesis Defense Announcement

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