DARTIC: Decentralized Anonymous Reputation at Scale for Trustworthy Crowdsourcing

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 <tex-math notation="LaTeX">DARTIC</tex-math><alternatives>DARTIC</alternatives>, a decentralized, anonymous, and scalable reputation-driven framework for crowdsourcing. <tex-math notation="LaTeX">DARTIC</tex-math><alternatives>DARTIC</alternatives> 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 <tex-math notation="LaTeX">DARTIC</tex-math><alternatives>DARTIC</alternatives> in both crowdsensing and federated learning scenarios. Experimental results show that (i) individual proof generation for token spending completes in less than 3 s, (ii) aggregation reduces the verification time of 1024 proofs from 8.7 s to 0.96 s, and (iii) zk-batching lowers gas costs by more than 100× 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.