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：链上众包利用区块链的去中心化、透明性和防篡改特性，构建可信且可验证的Web3众包服务。然而，现有去中心化声誉框架无法同时满足匿名性、声誉绑定和可扩展性。本文展示了如何在最小信任模型下，通过链上众包同时实现这些需求。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采用双账本系统，使请求者和工作者能在不同交互中使用不同假名，在确保不可关联性的同时维持可问责性。为缓解女巫攻击和声誉重置攻击，我们采用基于zkSNARK的集合成员证明，将用户所有假名加密绑定至单一访问令牌而不暴露关联关系。针对可扩展性，我们研究了两种聚合技术，将多个证明压缩为单个简洁证明以最小化验证开销。此外，我们设计了一种自动化隐私保护声誉模型，可动态评估不同众包场景下的贡献。为验证实用性，我们在群智感知和联邦学习场景中实例化并评估了DARTIC。实验结果表明：（i）单次令牌花费的证明生成耗时低于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）与纯第一层部署相比，ZK批处理可将Gas成本降低100倍以上。这些结果表明，完全去中心化的众包系统能够同时实现匿名性、强声誉绑定和可扩展性。