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arXiv 2403.04779cs.CRcs.GT
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面向区块链去中心化市场中信任与声誉即服务的研究

Towards Trust and Reputation as a Service in a Blockchain-based Decentralized Marketplace

Stephen Olariu, Ravi Mukkamala, Meshari Aljohani

AI总结:

针对社会5.0去中心化市场的可信服务难题,提出受拉普拉斯继承法则启发、由智能合约自动反馈的信任与声誉服务,覆盖多细分市场防欺诈、时变表现折扣、远期预测三类应用,经仿真验证分析结果有效。

摘要:

受社会5.0倡议中实现可信服务固有挑战的驱动,我们提出了一种适用于去中心化市场的新型信任与声誉服务。我们假设每笔交易都关联一个Smart Contract(智能合约),且该智能合约负责提供自动反馈,通过对各方履行义务的情况进行更客观的评估,替代出了名不可靠的买家反馈。我们的信任与声誉服务受Laplace Law of Succession(拉普拉斯继承法则)启发,其中对卖家的信任被定义为其在下一笔交易中履行义务的概率。我们提供了三种应用场景。首先,我们讨论了在多细分市场中的应用:恶意卖家可能通过售卖廉价商品建立极佳声誉,转而利用其良好声誉在另一个市场细分领域欺诈买家。其次,我们通过两种折扣方案展示了信任与声誉服务在卖家表现随时间变化场景下的运作机制,这两种方案会赋予旧声誉分数低于近期声誉分数的权重。最后,我们展示了如何基于不完整信息预测远期的信任与声誉。大量仿真实验已验证了我们的分析结果。

英文摘要:

Motivated by the challenges inherent in implementing trusted services in the Society 5.0 initiative, we propose a novel trust and reputation service for a decentralized marketplace. We assume that a Smart Contract is associated with each transaction and that the Smart Contract is responsible for providing automatic feedback, replacing notoriously unreliable buyer feedback by a more objective assessment of how well the parties have fulfilled their obligations. Our trust and reputation service was inspired by Laplace Law of Succession, where trust in a seller is defined as the probability that she will fulfill her obligations on the next transaction. We offer three applications. First, we discuss an application to a multi-segment marketplace, where a malicious seller may establish a stellar reputation by selling cheap items, only to use their excellent reputation to defraud buyers in a different market segment. Next, we demonstrate how our trust and reputation service works in the context of sellers with time-varying performance by providing two discounting schemes wherein older reputation scores are given less weight than more recent ones. Finally, we show how to predict trust and reputation far in the future, based on incomplete information. Extensive simulations have confirmed our analytical results.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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