Date of Award

11-21-2019

Publication Type

Master Thesis

Degree Name

M.Sc.

Department

Computer Science

First Advisor

Saeed Samet

Keywords

Blockchain

Rights

info:eu-repo/semantics/openAccess

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Abstract

Blockchain is a decentralized and peer-to-peer ledger technology that adds transparency, traceability, and immutability to data. It has shown great promise in mitigating the interoperability problem and privacy concerns in the de facto electronic health record anagement 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 applications, such as education, regulation, research, public health improvement, and policy sup- port. However, existing privacy acts prohibit health institutions and providers from sharing patients' data with third parties. Therefore, research institutions that con- duct 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 statis- tical 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 bene ts 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. Blockchain is a decentralized and peer-to-peer ledger technology that adds transparency, traceability, and immutability to data. It has shown great promise in mitigating the interoperability problem and privacy concerns in the de facto electronic health record anagement 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 applications, such as education, regulation, research, public health improvement, and policy sup- port. However, existing privacy acts prohibit health institutions and providers from sharing patients' data with third parties. Therefore, research institutions that con- duct 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 statis- tical 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 bene ts 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.

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