Graph-Based Profiling of Blockchain Oracles

The usage of blockchain technology has been significantly expanded with smart contracts and blockchain oracles. While smart contracts enables to automate the execution of an agreement between untrusted parties, oracles provide smart contracts with data external to a given blockchain, i.e., off-chain...
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Autor*in:	Khaled Almi'Ani [verfasserIn] Young Choon Lee [verfasserIn] Tawfiq Alrawashdeh [verfasserIn] Amirmohammad Pasdar [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	The blockchain oracle problem smart contracts distributed ledger technology Ethereum decentralized applications

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 11(2023), Seite 24995-25007
Übergeordnetes Werk:	volume:11 ; year:2023 ; pages:24995-25007

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2023.3254535

Katalog-ID:	DOAJ08785628X

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spelling	10.1109/ACCESS.2023.3254535 doi (DE-627)DOAJ08785628X (DE-599)DOAJfbf4c0d4c16c49459d66f3ac1bfe51a1 DE-627 ger DE-627 rakwb eng TK1-9971 Khaled Almi'Ani verfasserin aut Graph-Based Profiling of Blockchain Oracles 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The usage of blockchain technology has been significantly expanded with smart contracts and blockchain oracles. While smart contracts enables to automate the execution of an agreement between untrusted parties, oracles provide smart contracts with data external to a given blockchain, i.e., off-chain data. However, the validity and accuracy of such off-chain data can be questionable that compromises the transparency and immutability chacteristics of blockchain. Despite many studies on the trustworthiness of blockchain oracles, more precisely, off-chain data, their solutions are often ‘short-sighted’ and dependent on binary decisions. In this paper, we present a novel graph-based profiling method to determine the trustworthiness of blockchain oracles. We construct a graph with oracles as nodes and cumulative average discrepancies of validity and accuracy of data as edge weights. Our profiling method continues to update the graph, edge weights in particular, to distinguish trustworthy oracles. Clearly, this discourages the provision of false and inaccurate data. We have conducted an evaluation study to see the effectiveness of our proposed method, in which we have run the experiments utilizing the Ethereum network. Additionally, we have also calculated the cost of running these experiments. Consequently, our experiment results show that the proposed method achieves around 93% accuracy in identifying the trustworthiness of data sources. The blockchain oracle problem smart contracts distributed ledger technology Ethereum decentralized applications Electrical engineering. Electronics. Nuclear engineering Young Choon Lee verfasserin aut Tawfiq Alrawashdeh verfasserin aut Amirmohammad Pasdar verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 24995-25007 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:24995-25007 https://doi.org/10.1109/ACCESS.2023.3254535 kostenfrei https://doaj.org/article/fbf4c0d4c16c49459d66f3ac1bfe51a1 kostenfrei https://ieeexplore.ieee.org/document/10064295/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 24995-25007
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allfieldsSound	10.1109/ACCESS.2023.3254535 doi (DE-627)DOAJ08785628X (DE-599)DOAJfbf4c0d4c16c49459d66f3ac1bfe51a1 DE-627 ger DE-627 rakwb eng TK1-9971 Khaled Almi'Ani verfasserin aut Graph-Based Profiling of Blockchain Oracles 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The usage of blockchain technology has been significantly expanded with smart contracts and blockchain oracles. While smart contracts enables to automate the execution of an agreement between untrusted parties, oracles provide smart contracts with data external to a given blockchain, i.e., off-chain data. However, the validity and accuracy of such off-chain data can be questionable that compromises the transparency and immutability chacteristics of blockchain. Despite many studies on the trustworthiness of blockchain oracles, more precisely, off-chain data, their solutions are often ‘short-sighted’ and dependent on binary decisions. In this paper, we present a novel graph-based profiling method to determine the trustworthiness of blockchain oracles. We construct a graph with oracles as nodes and cumulative average discrepancies of validity and accuracy of data as edge weights. Our profiling method continues to update the graph, edge weights in particular, to distinguish trustworthy oracles. Clearly, this discourages the provision of false and inaccurate data. We have conducted an evaluation study to see the effectiveness of our proposed method, in which we have run the experiments utilizing the Ethereum network. Additionally, we have also calculated the cost of running these experiments. Consequently, our experiment results show that the proposed method achieves around 93% accuracy in identifying the trustworthiness of data sources. The blockchain oracle problem smart contracts distributed ledger technology Ethereum decentralized applications Electrical engineering. Electronics. Nuclear engineering Young Choon Lee verfasserin aut Tawfiq Alrawashdeh verfasserin aut Amirmohammad Pasdar verfasserin aut In IEEE Access IEEE, 2014 11(2023), Seite 24995-25007 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:11 year:2023 pages:24995-25007 https://doi.org/10.1109/ACCESS.2023.3254535 kostenfrei https://doaj.org/article/fbf4c0d4c16c49459d66f3ac1bfe51a1 kostenfrei https://ieeexplore.ieee.org/document/10064295/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2023 24995-25007
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source	In IEEE Access 11(2023), Seite 24995-25007 volume:11 year:2023 pages:24995-25007
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topic_facet	The blockchain oracle problem smart contracts distributed ledger technology Ethereum decentralized applications Electrical engineering. Electronics. Nuclear engineering
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callnumber-first	T - Technology
author	Khaled Almi'Ani
spellingShingle	Khaled Almi'Ani misc TK1-9971 misc The blockchain oracle problem misc smart contracts misc distributed ledger technology misc Ethereum misc decentralized applications misc Electrical engineering. Electronics. Nuclear engineering Graph-Based Profiling of Blockchain Oracles
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topic_title	TK1-9971 Graph-Based Profiling of Blockchain Oracles The blockchain oracle problem smart contracts distributed ledger technology Ethereum decentralized applications
topic	misc TK1-9971 misc The blockchain oracle problem misc smart contracts misc distributed ledger technology misc Ethereum misc decentralized applications misc Electrical engineering. Electronics. Nuclear engineering
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title	Graph-Based Profiling of Blockchain Oracles
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abstract	The usage of blockchain technology has been significantly expanded with smart contracts and blockchain oracles. While smart contracts enables to automate the execution of an agreement between untrusted parties, oracles provide smart contracts with data external to a given blockchain, i.e., off-chain data. However, the validity and accuracy of such off-chain data can be questionable that compromises the transparency and immutability chacteristics of blockchain. Despite many studies on the trustworthiness of blockchain oracles, more precisely, off-chain data, their solutions are often ‘short-sighted’ and dependent on binary decisions. In this paper, we present a novel graph-based profiling method to determine the trustworthiness of blockchain oracles. We construct a graph with oracles as nodes and cumulative average discrepancies of validity and accuracy of data as edge weights. Our profiling method continues to update the graph, edge weights in particular, to distinguish trustworthy oracles. Clearly, this discourages the provision of false and inaccurate data. We have conducted an evaluation study to see the effectiveness of our proposed method, in which we have run the experiments utilizing the Ethereum network. Additionally, we have also calculated the cost of running these experiments. Consequently, our experiment results show that the proposed method achieves around 93% accuracy in identifying the trustworthiness of data sources.
abstractGer	The usage of blockchain technology has been significantly expanded with smart contracts and blockchain oracles. While smart contracts enables to automate the execution of an agreement between untrusted parties, oracles provide smart contracts with data external to a given blockchain, i.e., off-chain data. However, the validity and accuracy of such off-chain data can be questionable that compromises the transparency and immutability chacteristics of blockchain. Despite many studies on the trustworthiness of blockchain oracles, more precisely, off-chain data, their solutions are often ‘short-sighted’ and dependent on binary decisions. In this paper, we present a novel graph-based profiling method to determine the trustworthiness of blockchain oracles. We construct a graph with oracles as nodes and cumulative average discrepancies of validity and accuracy of data as edge weights. Our profiling method continues to update the graph, edge weights in particular, to distinguish trustworthy oracles. Clearly, this discourages the provision of false and inaccurate data. We have conducted an evaluation study to see the effectiveness of our proposed method, in which we have run the experiments utilizing the Ethereum network. Additionally, we have also calculated the cost of running these experiments. Consequently, our experiment results show that the proposed method achieves around 93% accuracy in identifying the trustworthiness of data sources.
abstract_unstemmed	The usage of blockchain technology has been significantly expanded with smart contracts and blockchain oracles. While smart contracts enables to automate the execution of an agreement between untrusted parties, oracles provide smart contracts with data external to a given blockchain, i.e., off-chain data. However, the validity and accuracy of such off-chain data can be questionable that compromises the transparency and immutability chacteristics of blockchain. Despite many studies on the trustworthiness of blockchain oracles, more precisely, off-chain data, their solutions are often ‘short-sighted’ and dependent on binary decisions. In this paper, we present a novel graph-based profiling method to determine the trustworthiness of blockchain oracles. We construct a graph with oracles as nodes and cumulative average discrepancies of validity and accuracy of data as edge weights. Our profiling method continues to update the graph, edge weights in particular, to distinguish trustworthy oracles. Clearly, this discourages the provision of false and inaccurate data. We have conducted an evaluation study to see the effectiveness of our proposed method, in which we have run the experiments utilizing the Ethereum network. Additionally, we have also calculated the cost of running these experiments. Consequently, our experiment results show that the proposed method achieves around 93% accuracy in identifying the trustworthiness of data sources.
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title_short	Graph-Based Profiling of Blockchain Oracles
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