GoCrowd: Obliviously Aggregating Crowd Wisdom With Quality Awareness in Crowdsourcing

Organizations these days capitalize on crowdsourcing to learn collective wisdom from a population of individuals. Vast amounts of data have been gathered, making the crowdsourcing platforms a lucrative target to steal data from and thus raising severe privacy concerns. Data contributed by workers may carry sensitive individual information. Meanwhile, organizations deem the aggregate statistics as intellectual property. In this paper, we propose, design, and evaluate GoCrowd, a system framework for obliviously aggregating wisdom with quality assurance in crowdsourcing. At its core, we propose constructions for two procedures. The starting point is a gold-standard based private worker quality control procedure that provides privacy-friendly worker quality assurance under the widely popular gold-standard mechanism. The subsequent procedure is an oblivious wisdom aggregation procedure that obliviously learns aggregate statistics over workers' data while considering their quality. We securely realize these procedures with only lightweight secret sharing techniques. Our system is utterly oblivious to the service provider, and ensures that only the requester can learn the aggregate quality-aware statistics but nothing more. Extensive evaluations show that GoCrowd can produce quality statistics over data from 500 workers for 200 16-choice questions within 1 s.