  Crowdsourcing has emerged as a prevalent method for mitigating the risks of
correctness and security in outsourced cloud computing. This process involves
an aggregator distributing tasks, collecting responses, and aggregating
outcomes from multiple data sources. Such an approach harnesses the wisdom of
crowds to accomplish complex tasks, enhancing the accuracy of task completion
while diminishing the risks associated with the malicious actions of any single
entity. However, a critical question arises: How can we ensure that the
aggregator performs its role honestly and each contributor's input is fairly
evaluated? In response to this challenge, we introduce a novel protocol termed
$\mathsf{zkTI}. This scheme guarantees both the honest execution of the
aggregation process by the aggregator and the fair evaluation of each data
source. It innovatively integrates a cryptographic construct known as
zero-knowledge proof with a category of truth inference algorithms for the
first time. Under this protocol, the aggregation operates with both correctness
and verifiability, while ensuring fair assessment of data source reliability.
Experimental results demonstrate the protocol's efficiency and robustness,
making it a viable and effective solution in crowdsourcing and cloud computing.
