  With the rapid popularity of blockchain, decentralized human intelligence
tasks (HITs) are proposed to crowdsource human knowledge without relying on
vulnerable third-party platforms. However, the inherent limits of blockchain
cause decentralized HITs to face a few "new" challenges. For example, the
confidentiality of solicited data turns out to be the sine qua non, though it
was an arguably dispensable property in the centralized setting. To ensure the
"new" requirement of data privacy, existing decentralized HITs use generic
zero-knowledge proof frameworks (e.g. SNARK), but scarcely perform well in
practice, due to the inherently expensive cost of generality.
  We present a practical decentralized protocol for HITs, which also achieves
the fairness between requesters and workers. At the core of our contributions,
we avoid the powerful yet highly-costly generic zk-proof tools and propose a
special-purpose scheme to prove the quality of encrypted data. By various
non-trivial statement reformations, proving the quality of encrypted data is
reduced to efficient verifiable decryption, thus making decentralized HITs
practical. Along the way, we rigorously define the ideal functionality of
decentralized HITs and then prove the security due to the ideal-real paradigm.
  We further instantiate our protocol to implement a system called Dragoon, an
instance of which is deployed atop Ethereum to facilitate an image annotation
task used by ImageNet. Our evaluations demonstrate its practicality: the
on-chain handling cost of Dragoon is even less than the handling fee of
Amazon's Mechanical Turk for the same ImageNet HIT.
