On-chain crowdsourcing leverages blockchain's decentralization, transparency, and tamper-resistance to build trustworthy and verifiable Web3 crowdsourced services. However, existing decentralized reputation frameworks do not reconcile anonymity, reputation binding, and scalability. This paper demonstrates how on-chain crowdsourcing can simultaneously achieve these requirements under a trust-minimized model. We introduce DARTIC, a decentralized, anonymous, and scalable reputation-driven framework for crowdsourcing. DARTIC presents a dual-ledger system that enables requesters and workers to use distinct pseudonyms across interactions, ensuring unlinkability while maintaining accountability. To mitigate Sybil and reputation-reset attacks, we employ zkSNARK-based set membership proofs, cryptographically binding all user pseudonyms to a single access token without revealing the linkage. For scalability, we investigate two aggregation techniques that compress multiple proofs into a single succinct proof to minimize verification overhead. In addition, we design an automated, privacy-preserving reputation model that dynamically evaluates contributions across diverse crowdsourcing contexts. To demonstrate practicality, we instantiate and assess DARTIC in both crowdsensing and federated learning scenarios. Experimental results show that (i) individual proof generation for token spending completes in less than 3s, (ii) aggregation reduces the verification time of 1024 proofs from 8.7s to 0.96s, and (iii) zk-batching lowers gas costs by more than 100x compared to a pure Layer-1 deployment. These results demonstrate that anonymity, robust reputation binding, and scalability can be jointly achieved in fully decentralized crowdsourcing systems.

翻译：链上众包借助区块链的去中心化、透明性与防篡改特性，构建可信可验证的Web3众包服务。然而，现有去中心化声誉框架难以同时实现匿名性、声誉绑定与可扩展性需求。本文论证了在最小信任模型下，链上众包如何同步达成上述要求。我们提出DARTIC——一种面向众包的、去中心化、匿名且可扩展的声誉驱动框架。DARTIC采用双账本体系，使请求者与工作者在交互中使用不同假名，在保证不可关联性的同时维持问责制。为抵御女巫攻击与声誉重置攻击，我们采用基于zkSNARK的集合成员证明，以密码学方式将所有用户假名绑定至单一访问令牌，且不泄露关联关系。在可扩展性方面，我们研究了两种聚合技术，可将多个证明压缩为单一简洁证明以最小化验证开销。此外，我们设计了自动化的隐私保护声誉模型，可动态评估不同众包场景下的贡献。为验证实用性，我们在群智感知与联邦学习场景中实例化并评估DARTIC。实验结果表明：(i) 代币消费的单个证明生成时间少于3秒；(ii) 聚合技术将1024个证明的验证时间从8.7秒降至0.96秒；(iii) 相较于纯Layer-1部署，零知识批处理将Gas成本降低超过100倍。上述结果表明，完全去中心化的众包系统可同时实现匿名性、强声誉绑定与可扩展性。