DARTIC: Decentralized Anonymous Reputation at Scale for Trustworthy Crowdsourcing

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AI Summary

Existing decentralized crowdsourcing systems struggle to simultaneously achieve anonymity, reputation binding, and scalability. This work proposes the first design that cohesively integrates all three properties within a fully decentralized setting. By employing a dual-ledger architecture, users can interact across tasks using unlinkable pseudonyms, while a Sybil-resistant, privacy-preserving reputation model is realized through zkSNARKs. The system innovatively incorporates set membership proofs, proof aggregation, and zk-batching techniques to ensure accountability without compromising efficiency. Empirical results demonstrate significant performance gains: individual proof generation takes under 3 seconds, and verification time for 1,024 proofs drops from 8.7 seconds to 0.96 seconds. Moreover, gas costs are reduced by over two orders of magnitude compared to a pure Layer-1 solution.

Abstract

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.

Problem

• decentralized reputation
• anonymity
• scalability
• Sybil attacks
• blockchain crowdsourcing

Innovation

• zkSNARK
• decentralized reputation
• anonymous credentials
• proof aggregation
• Sybil resistance