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Instant Resonance: Dual Strategy Enhances the Data Consensus Success Rate of Blockchain Threshold Signature Oracles

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arxiv 2411.02945 v1 pith:C5IQZDLD submitted 2024-11-05 cs.DC cs.ET

classification cs.DCcs.ET
keywords datasuccessconsensusthresholdnodesoraclessignatureblockchain
verification ladder T0 review T1 audit T2 compute T3 formal
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With the rapid development of Decentralized Finance (DeFi) and Real-World Assets (RWA), the importance of blockchain oracles in real-time data acquisition has become increasingly prominent. Using cryptographic techniques, threshold signature oracles can achieve consensus on data from multiple nodes and provide corresponding proofs to ensure the credibility and security of the information. However, in real-time data acquisition, threshold signature methods face challenges such as data inconsistency and low success rates in heterogeneous environments, which limit their practical application potential. To address these issues, this paper proposes an innovative dual-strategy approach to enhance the success rate of data consensus in blockchain threshold signature oracles. Firstly, we introduce a Representative Enhanced Aggregation Strategy (REP-AG) that improves the representativeness of data submitted by nodes, ensuring consistency with data from other nodes, and thereby enhancing the usability of threshold signatures. Additionally, we present a Timing Optimization Strategy (TIM-OPT) that dynamically adjusts the timing of nodes' access to data sources to maximize consensus success rates. Experimental results indicate that REP-AG improves the aggregation success rate by approximately 56.6\% compared to the optimal baseline, while the implementation of TIM-OPT leads to an average increase of approximately 32.9\% in consensus success rates across all scenarios.

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Cited by 1 Pith paper

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  1. Connecting Large Language Models with Blockchain: Advancing the Evolution of Smart Contracts from Automation to Intelligence

    cs.DC 2024-12 conditional novelty 6.0 of 10

    C-LLM is a framework for feeding LLM answers into blockchain smart contracts via oracles, and SenteTruth aggregates textual answers using SBERT semantic similarity plus truth discovery to resist up to 40% malicious nodes.

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