Title: ZebraLancer: Crowdsource Knowledge atop Open Blockchain, Privately and Anonymously,
Abstract: We design and implement the first private and anonymous decentralized
crowdsourcing system ZebraLancer. It realizes the fair exchange (i.e. security
against malicious workers and dishonest requesters) without using any
third-party arbiter. More importantly, it overcomes two fundamental challenges
of decentralization, i.e. data leakage and identity breach.
First, our outsource-then-prove methodology resolves the critical tension
between blockchain transparency and data confidentiality without sacrificing
the fairness of exchange. ZebraLancer ensures: a requester will not pay more
than what data deserve, according to a policy announced when her task is
published through the blockchain; each worker indeed gets a payment based on
the policy, if submits data to the blockchain; the above properties are
realized not only without a central arbiter, but also without leaking the data
to blockchain network.
Furthermore, the blockchain transparency might allow one to infer private
information of workers/requesters through their participation history.
ZebraLancer solves the problem by allowing anonymous participations without
surrendering user accountability. Specifically, workers cannot misuse anonymity
to submit multiple times to reap rewards, and an anonymous requester cannot
maliciously submit colluded answers to herself to repudiate payments. The idea
behind is a subtle linkability: if one authenticates twice in a task, everybody
can tell, or else staying anonymous. To realize such delicate linkability, we
put forth a novel cryptographic notion, the common-prefix-linkable anonymous
authentication.
Finally, we implement our protocol for a common image annotation task and
deploy it in a test net of Ethereum. The experiment results show the
applicability of our protocol and highlight subtleties of tailoring the
protocol to be compatible with the existing real-world open blockchain.