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 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.

Automated summary

The usage of blockchain technology has been significantly expanded with the introduction of smart contracts and blockchain oracles. However, the validity and accuracy of off-chain data provided by oracles can be questionable, compromising the transparency and immutability of blockchain. In this paper, a graph-based profiling method is proposed to determine the trustworthiness of blockchain oracles by constructing a graph with oracles as nodes and cumulative average discrepancies of data as edge weights. The evaluation study conducted on the Ethereum network demonstrates an accuracy of around 93% in identifying trustworthy data sources.