Abstract
The rapid development of wearable sensors and the 5G network empowers traditional medical treatment with the ability to collect patients’ information remotely for monitoring and diagnosing purposes. Meanwhile, the health-related mobile apps and devices also generate a large amount of medical data, which is critical for promoting disease research and diagnosis. However, medical data is too sensitive to share, which is also a common issue for IoT (Internet of Things) data. The traditional centralized cloud-based medical data sharing schemes have to rely on a single trusted third party. Therefore, the schemes suffer from single-point failure and lack of privacy protection and access control for the data. Blockchain is an emerging technique to provide an approach for managing data in a decentralized manner. Especially, the blockchain-based smart contract technique enables the programmability for participants to access the data. All the interactions are authenticated and recorded by the other participants of the blockchain network, which is tamper resistant. In this paper, we leverage the K-anonymity and searchable encryption techniques and propose a blockchain-based privacy-preserving scheme for medical data sharing among medical institutions and data users. To be specific, the consortium blockchain, Hyperledger Fabric, is adopted to allow data users to search for encrypted medical data records. The smart contract, i.e., the chaincode, implements the attribute-based access control mechanisms to guarantee that the data can only be accessed by the user with proper attributes. The K-anonymity and searchable encryption ensure that the medical data is shared without privacy leaking, i.e., figuring out an individual patient from queries. We implement a prototype system using the chaincode of Hyperledger Fabric. From the functional perspective, security analysis shows that the proposed scheme satisfies security goals and precedes others. From the performance perspective, we conduct experiments by simulating different numbers of medical institutions. The experimental results demonstrate that the scalability and performance of our scheme are practical.
Highlights
Data sharing is crucial for promoting the research of disease tracking and treatment
To tackle the two challenges mentioned above, the contributions of our developed system are as follows: (i) We adopt the K-anonymity technique to preprocess the data for privacy preserving (ii) We design the scheme based on searchable encryption for storing the encrypted medical data on clouds and enable the keyword search in a privacy-preserving manner (iii) We develop smart contracts based on Hyperledger Fabric, and realize the secure keyword search and the attribute-based access control model (iv) We implement a prototype system with smart contracts based on a chaincode of Hyperledger Fabric and conduct experiments with simulating different numbers of institutions (v) We analyze the security properties and evaluate the computational overhead
We have presented a consortium blockchain-based medical data sharing system using K-anonymity, keyword searchable encryption, and Attribute-based access control (ABAC) to achieve data privacy-preserving and security among different medical institutions
Summary
Data sharing is crucial for promoting the research of disease tracking and treatment. Traditional approaches are based on clouds to store data remotely and different institutions share the data in a centralized manner. We propose a privacy-preserving scheme based on the blockchain for medical data sharing. (i) We adopt the K-anonymity technique to preprocess the data for privacy preserving (ii) We design the scheme based on searchable encryption for storing the encrypted medical data on clouds and enable the keyword search in a privacy-preserving manner (iii) We develop smart contracts based on Hyperledger Fabric, and realize the secure keyword search and the attribute-based access control model (iv) We implement a prototype system with smart contracts based on a chaincode of Hyperledger Fabric (the URL of source code: https://github .com/mythsand/privacy-preserving-medical-data) and conduct experiments with simulating different numbers of institutions (v) We analyze the security properties and evaluate the computational overhead.
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