用AI和零知识技术构建链上信用评分,识别可信用户
zScore: A Universal Decentralised Reputation System for the Blockchain Economy
- 基于钱包行为与零知识凭证,用AI模型计算链上信用分
- 测试显示高分者借贷还款更健康,相关性显著提升
- 适合防欺诈、激励真实价值创造的去中心化应用
现代社会依赖信任,而链上经济在无中心信任机构的对抗环境中运行,亟需可验证的信誉量化系统以减少不良经济行为。本文提出zScore框架,通过先进AI神经网络模型分析钱包的链上行为,并结合通过zkTLS上链的真实世界凭证,构建可信赖的声誉体系。在借贷协议的历史数据回溯测试中,高zScore与健康的借贷及还款行为呈现强相关性,证明其作为信用背书的稳健性,相比Cred等早期方案有显著提升。文中还列举了该系统的多种应用场景,可用于奖励真实价值创造、过滤噪声与可疑活动,并识别恶意行为者。
原文摘要 · Abstract (English)
Modern society functions on trust. The onchain economy, however, is built on the founding principles of trustless peer-to-peer interactions in an adversarial environment without a centralised body of trust and needs a verifiable system to quantify credibility to minimise bad economic activity. We provide a robust framework titled zScore, a core primitive for reputation derived from a wallet's onchain behaviour using state-of-the-art AI neural network models combined with real-world credentials ported onchain through zkTLS. The initial results tested on retroactive data from lending protocols establish a strong correlation between a good zScore and healthy borrowing and repayment behaviour, making it a robust and decentralised alibi for creditworthiness; we highlight significant improvements from previous attempts by protocols like Cred showcasing its robustness. We also present a list of possible applications of our system in Section 5, thereby establishing its utility in rewarding actual value creation while filtering noise and suspicious activity and flagging malicious behaviour by bad actors.
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