A Hybrid Blockchain-Edge Architecture for Electronic Health Record Management With Attribute-Based Cryptographic Mechanisms

Guo, Hao; Li, Wanxin; Nejad, Mark; Shen, Chien-Chung

doi:10.1109/tnsm.2022.3186006

Cited by 20 publications

(10 citation statements)

References 44 publications

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“…The novel concept enabled application scripts in terms of their deployment and execution in a safe environment named the Ethereum Virtual Machine (EVM) on distributed nodes in the form of decentralized applications (DApps). In the Cloud domain, smart contracts became extremely popular as a tool to improve existing environments [ 7 , 8 , 9 ] and even addressed different requirements such as privacy-preserving data sharing [ 10 ]. With the introduction of dedicated optimized variations of Ethereum (e.g., BNB chain ( (accessed on 3 October 2022)), Polygon ( (accessed on 7 October 2022)), Optimism ( (accessed on 9 October 2022)), and many others) using the same EVM core engine, the sustainability of the smart contract use case increased.…”

Section: Related Workmentioning

confidence: 99%

A Recommender System for Robust Smart Contract Template Classification

Gec

Stankovski²,

Lavbič³

et al. 2023

Sensors

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IoT environments are becoming increasingly heterogeneous in terms of their distributions and included entities by collaboratively involving not only data centers known from Cloud computing but also the different types of third-party entities that can provide computing resources. To transparently provide such resources and facilitate trust between the involved entities, it is necessary to develop and implement smart contracts. However, when developing smart contracts, developers face many challenges and concerns, such as security, contracts’ correctness, a lack of documentation and/or design patterns, and others. To address this problem, we propose a new recommender system to facilitate the development and implementation of low-cost EVM-enabled smart contracts. The recommender system’s algorithm provides the smart contract developer with smart contract templates that match their requirements and that are relevant to the typology of the fog architecture. It mainly relies on OpenZeppelin, a modular, reusable, and secure smart contract library that we use when classifying the smart contracts. The evaluation results indicate that by using our solution, the smart contracts’ development times are overall reduced. Moreover, such smart contracts are sustainable for fog-computing IoT environments and applications in low-cost EVM-based ledgers. The recommender system has been successfully implemented in the ONTOCHAIN ecosystem, thus presenting its applicability.

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Section: Related Workmentioning

confidence: 99%

A Recommender System for Robust Smart Contract Template Classification

Gec

Stankovski²,

Lavbič³

et al. 2023

Sensors

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“…The aggregated ZKP scheme is programmed by using the Hyperledger Ursa library 7 . The blockchain network is developed on the Hyperledger Fabric platform 8 and tested using the Hyperledger Caliper benchmark tool 9 . We then instantiate multiple participants in the blockchain network, including different autonomous trucks and companies, and perform a variety of experiments having varying endorsement policies and transaction send rates.…”

Section: A Implementationmentioning

confidence: 99%

“…As a fault tolerant and decentralized network technology, blockchain has become a hot research topic since its initial deployment in the Bitcoin protocol for use as a distributed digital currency ledger [7]. Lately, many research studies have highlighted the applicability of blockchain technology to a wide array of subject areas outside of cryptocurrency, such as healthcare [8]- [10], smart cities [11]- [13] and intelligent transportation systems (ITS) [14]- [16]. Specifically, there are two distinct forms of blockchain technology: permissionless and permissioned.…”

Section: Introductionmentioning

confidence: 99%

Location-aware Verification for Autonomous Truck Platooning Based on Blockchain and Zero-knowledge Proof

Meese

Zhong

et al. 2021

2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)

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Platooning technologies enable trucks to drive cooperatively and automatically, providing benefits including less fuel consumption, greater road capacity, and safety. To establish trust during dynamic platooning formation, ensure vehicular data integrity, and guard platoons against potential attackers in mixed fleet environments, verifying any given vehicle's identity information before granting it access to join a platoon is pivotal. Besides, due to privacy concerns, truck owners may be reluctant to disclose private vehicular information, which can reveal their business data to untrusted third parties. To address these issues, this is the first study to propose an aggregated zeroknowledge proof and blockchain-empowered system for privacypreserving identity verification in truck platooning. We provide the correctness proof and the security analysis of our proposed authentication scheme, highlighting its increased security and fast performance. The platooning formation procedure is re-designed to seamlessly incorporate the proposed authentication scheme, including the 1st catch-up and cooperative driving steps. The blockchain performs the role of verifier within the authentication scheme and stores platooning records on its digital ledger to guarantee data immutability and integrity. In addition, the proposed programmable access control policies enable truck companies to define who is allowed to access their platoon records. We implement the proposed system and perform extensive experiments on the Hyperledger platform. The results show that the blockchain can provide low latency and high throughput, the aggregated approach can offer a constant verification time of 500 milliseconds regardless of the number of proofs, and the platooning formation only takes seconds under different strategies. The experimental results demonstrate the feasibility of our design for use in real-world truck platooning.

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“…To address these existing drawbacks, homomorphic encryption [8] could be a potential solution. Compared to other cryptography techniques, homomorphic encryption is a form of encryption that permits users to perform computations on its encrypted data without decrypting it [8], [9]. Homomorphic encryption can be used for privacy-preserving outsourced storage and computation [9].…”

Section: Introductionmentioning

confidence: 99%

“…Compared to other cryptography techniques, homomorphic encryption is a form of encryption that permits users to perform computations on its encrypted data without decrypting it [8], [9]. Homomorphic encryption can be used for privacy-preserving outsourced storage and computation [9]. It allows data to be encrypted and outsourced to a federated learning server or other computing devices.…”

Section: Introductionmentioning

confidence: 99%

B²SFL: A Bi-Level Blockchained Architecture for Secure Federated Learning-Based Traffic Prediction

Guo,

Meese,

et al. 2023

IEEE Trans. Serv. Comput.

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Federated Learning (FL) is a privacy-preserving machine learning (ML) technology that enables collaborative training and learning of a global ML model based on aggregating distributed local model updates. However, security and privacy guarantees could be compromised due to malicious participants and the centralized FL server. This paper proposed a bi-level blockchained architecture for secure federated learning-based traffic prediction. The bottom and top layer blockchain store the local model and global aggregated parameters accordingly, and the distributed homomorphic-encrypted federated averaging (DHFA) scheme addresses the secure computation problems. We propose the partial private key distribution protocol and a partially homomorphic encryption/decryption scheme to achieve the distributed privacy-preserving federated averaging model. We conduct extensive experiments to measure the running time of DHFA operations, quantify the read and write performance of the blockchain network, and elucidate the impacts of varying regional group sizes and model complexities on the resulting prediction accuracy for the online traffic flow prediction task. The results indicate that the proposed system can facilitate secure and decentralized federated learning for real-world traffic prediction tasks.

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A Hybrid Blockchain-Edge Architecture for Electronic Health Record Management With Attribute-Based Cryptographic Mechanisms

Cited by 20 publications

References 44 publications

A Recommender System for Robust Smart Contract Template Classification

A Recommender System for Robust Smart Contract Template Classification

Location-aware Verification for Autonomous Truck Platooning Based on Blockchain and Zero-knowledge Proof

B²SFL: A Bi-Level Blockchained Architecture for Secure Federated Learning-Based Traffic Prediction

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A Hybrid Blockchain-Edge Architecture for Electronic Health Record Management With Attribute-Based Cryptographic Mechanisms

Cited by 20 publications

References 44 publications

A Recommender System for Robust Smart Contract Template Classification

A Recommender System for Robust Smart Contract Template Classification

Location-aware Verification for Autonomous Truck Platooning Based on Blockchain and Zero-knowledge Proof

B2SFL: A Bi-Level Blockchained Architecture for Secure Federated Learning-Based Traffic Prediction

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Product

Resources

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B²SFL: A Bi-Level Blockchained Architecture for Secure Federated Learning-Based Traffic Prediction