HARB: A Hypergraph-Based Adaptive Consortium Blockchain for Decentralized Energy Trading

Karumba, Samuel; Kanhere, Salil S.; Jurdak, Raja; Sethuvenkatraman, Subbu

doi:10.1109/jiot.2020.3022045

Cited by 21 publications

(8 citation statements)

References 19 publications

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“…In the early stage of cross-chain interoperability, the relationship among cross-chain actions is certain, in which actions have no competition. It can be seen in the bid process of an e-commerce exchange [19] [20] or in the energy trading process [21] [22]. The participants and their actions are certain, and it can be abstracted as the all-trigger model.…”

Section: B Related Workmentioning

confidence: 99%

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Quantitative cooperation analysis among cross-chain smart contracts

Guo

et al. 2022

Neural Comput & Applic

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In cross-chain scenarios, there are different blockchains, which need to cooperate. Cooperation among different blockchains is done by smart contracts that work together to complete cross-chain tasks. When numerous cooperative smart contracts are involved, smart contracts form a complex interaction network, which makes it difficult to evaluate the cooperation. It needs a common model to quantitatively analyze the crosschain cooperation of associated smart contracts. In this paper, we model the cooperation among smart contracts as conditions and their corresponding actions, the condition-trigger model. Then we propose the method to calculate the cooperation probabilities by the graph weight. As the edge weight lacks the information of interaction probabilities, we introduce the dimension of the edge weight to calculate the probabilities. Finally, we verify the proposed condition-trigger model and its different types. It demonstrates that our proposed methods can effectively analyze the cross-chain cooperation among smart contracts.

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Section: B Related Workmentioning

confidence: 99%

“…As those two actions come from two dynamic branches of an action, the target can be triggered by any of them. The probability of the target action to be triggered is the sum of the probabilities of the two inputs, shown in (21).…”

Section: A Probabilities Calculation 1) Relationship Among Different ...mentioning

confidence: 99%

Quantitative cooperation analysis among cross-chain smart contracts

Guo

et al. 2022

Neural Comput & Applic

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Section: B Related Workmentioning

confidence: 99%

Quantitative Cooperation Analysis among Cross-chain Smart Contracts

Hong¹

2021

Preprint

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<div>In cross-chain scenarios, there are different blockchains, which need to cooperate. Cooperation among different blockchains is done by smart contracts that work together to complete cross-chain tasks. When numerous cooperative smart contracts are involved, smart contracts form a complex interaction network, which makes it difficult to evaluate the cooperation. It needs a common model to quantitatively analyze the cross-chain cooperation of associated smart contracts. In this paper, we model the cooperation among smart contracts as conditions and their corresponding actions, the condition-trigger model. Then we propose the method to calculate the cooperation probabilities by the graph weight. As the edge weight lacks the information of interaction probabilities, we introduce the dimension of the edge weight to calculate the probabilities. Finally, we verify the proposed condition-trigger model and its different types. It demonstrates that our proposed methods can effectively analyze the cross-chain cooperation among smart contracts.</div>

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“…Wang et al proposed a directed hypergraph-based learning scheme based on security enhanced content sharing in social IoT [178]. In addition, Karumba et al defined the HARB, which can be regarded as hypergraphbased adaptive consortium blockchain for decentralised energy trading [179].…”

Section: B Differential Privacy For Hypergraph Iiot Networkmentioning

confidence: 99%

Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

Jiang,

Li,

Yue

et al. 2021

Preprint

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The development of Internet of Things (IoT) brings new changes to various fields. Particularly, industrial Internet of Things (IIoT) is promoting a new round of industrial revolution. With more applications of IIoT, privacy protection issues are emerging. Specially, some common algorithms in IIoT technology such as deep models strongly rely on data collection, which leads to the risk of privacy disclosure. Recently, differential privacy has been used to protect user-terminal privacy in IIoT, so it is necessary to make in-depth research on this topic. In this paper, we conduct a comprehensive survey on the opportunities, applications and challenges of differential privacy in IIoT. We firstly review related papers on IIoT and privacy protection, respectively. Then we focus on the metrics of industrial data privacy, and analyze the contradiction between data utilization for deep models and individual privacy protection. Several valuable problems are summarized and new research ideas are put forward. In conclusion, this survey is dedicated to complete comprehensive summary and lay foundation for the follow-up researches on industrial differential privacy.

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HARB: A Hypergraph-Based Adaptive Consortium Blockchain for Decentralized Energy Trading

Cited by 21 publications

References 19 publications

Quantitative cooperation analysis among cross-chain smart contracts

Quantitative cooperation analysis among cross-chain smart contracts

Quantitative Cooperation Analysis among Cross-chain Smart Contracts

Differential Privacy for Industrial Internet of Things: Opportunities, Applications and Challenges

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