A Decentralized Truth Discovery Approach to the Blockchain Oracle Problem
A Decentralized Truth Discovery Approach to the Blockchain Oracle Problem
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DOI：
10.1109/infocom53939.2023.10229019
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发表时间：
2023-05
期刊：
影响因子：
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通讯作者：
Yang Xiao;Ning Zhang;W. Lou;Y. T. Hou
中科院分区：
文献类型：
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作者：
Yang Xiao;Ning Zhang;W. Lou;Y. T. Hou
当区块链应用程序从现实世界中运行时，它依赖于将数据从外部来源传输到区块链的机制，围绕着从外部来源获得可信赖的数据，从而避免了一个求解源源于erace orace当合法的外部资源提交fur虫或欺骗性数据时，出现了真实的数据挑战，我们在本文中仍未解决。从多链方式中获得多个输入的对象。启用Oracle服务方案与现有的基于中位数的聚合方法相比，分散图的拜占庭式弹性和长期数据进料的准确性明显更高。
When a blockchain application runs on data from the real world, it relies on an oracle mechanism that transports data from external sources to the blockchain. The blockchain oracle problem arises around the need to procure trustworthy data from external sources. Previous works have addressed data authenticity/integrity by building a secure channel between blockchain and external sources while employing a decentralized oracle network to avoid a single point of failure. However, the truthful data challenge, which emerges when legitimate external sources submit fraudulent or deceitful data, remains unsolved. In this paper, we introduce a new decentralized truth-discovering oracle architecture called DecenTruth to address the truthful data challenge using a data-centric approach. DecenTruth aims to elevate the "truthfulness" of external data input by enabling decentralized oracle nodes to discover and reach consensus on truthful values of common data objects from multi-sourced inputs in an off-chain manner. It harmonizes techniques in both the data plane and consensus plane—truth discovery (TD) and asynchronous BFT consensus—and enables nodes to finalize the same estimated truths on data objects with high accuracy, amid the harsh asynchronous network condition and presence of Byzantine sources and nodes. We implemented DecenTruth and evaluated its performance in a simulated oracle service scenario. The results demonstrate significantly higher Byzantine resilience and long-term data feed accuracy of DecenTruth, compared to existing median-based aggregation methods.