$\mathsf{BFCS}$: A Secure and Efficient Service Framework for Bribery-Free Crowdsourcing

Crowdsourcing is widely used to solve complex problems by leveraging a large pool of potentially unreliable submitters. To tolerate such unreliability and ensure high-quality outcomes, recent studies have proposed various methods, such as quality-aware incentive mechanisms. However, these approaches only address issues arising from in-system submitter malfeasance or failures, while overlooking the more harmful threat of external bribery attacks. In practice, malicious external parties can bribe submitters to manipulate their submissions, posing serious risks in sensitive fields such as finance, government, and healthcare, where the consequences of successful bribery far outweigh the cost. In response, we propose BFCS, a secure and efficient service framework for Bribery-Free Crowd Sourcing. The core idea of BFCS is to use the “re-encryption” technique to disconnect submitters from their submissions. Specifically, after submitters encrypt their data to protect privacy, they do not send it directly to the requester. Instead, it is first re-encrypted by a trusted randomizer. This process introduces randomness that prevents bribers from tracing a submission's origin, thereby eliminating bribery risks. To further enhance security, BFCS incorporates: (1) a decentralized secure re-encryption protocol to enable secure re-encryption in trust-scarce crowdsourcing environments; (2) an adaptive anonymous reward distribution scheme to preserve the bribery-free property during crowdsourcing reward allocation; and (3) a smart contract-based fair staking mechanism to balance the fairness of reward allocation with bribery-free guarantees. Our theoretical analysis and experiments on three typical crowdsourcing tasks—image annotation, text annotation, and sensor data collection—demonstrate that BFCS is both secure and efficient, making it well suited for real-world deployments and effective in preventing submitter bribery