Due to the risks of correctness and security in outsourced cloud computing, we consider a new paradigm called crowdsourcing: distribute tasks, receive answers and aggregate the results from multiple entities. Through this approach, we can aggregate the wisdom of the crowd to complete tasks, ensuring the accuracy of task completion while reducing the risks posed by the malicious acts of a single entity. However, the ensuing question is, how can we ensure that the aggregator has done its work honestly and each contributor's work has been evaluated fairly? In this paper, we propose a new scheme called $\mathsf{zkTI}$. This scheme ensures that the aggregator has honestly completed the aggregation and each data source is fairly evaluated. We combine a cryptographic primitive called \textit{zero-knowledge proof} with a class of \textit{truth inference algorithms} which is widely studied in AI/ML scenarios. Under this scheme, various complex outsourced tasks can be solved with efficiency and accuracy. To build our scheme, a novel method to prove the precise computation of floating-point numbers is proposed, which is nearly optimal and well-compatible with existing argument systems. This may become an independent point of interest. Thus our work can prove the process of aggregation and inference without loss of precision. We fully implement and evaluate our ideas. Compared with recent works, our scheme achieves $2-4 \times$ efficiency improvement and is robust to be widely applied.

翻译：由于外包云计算存在正确性与安全性风险，我们提出一种称为"众包"的新范式：通过向多个实体分发任务、接收答案并聚合结果，可汇聚群体智慧完成任务，在确保任务完成准确性的同时降低单个实体恶意行为带来的风险。然而随之而来的问题是：如何保证聚合者诚实完成工作，且每个贡献者的工作得到公平评估？本文提出一种名为$\mathsf{zkTI}$的新方案。该方案通过将密码学原语——零知识证明与人工智能/机器学习场景中广泛研究的真值推断算法相结合，确保聚合者诚实完成聚合过程，且每个数据源均受到公平评估。在该框架下，各类复杂外包任务可高效精准地完成。为构建该方案，我们提出了一种近乎最优且与现有论证系统高度兼容的浮点数精确计算证明新方法，该技术本身即可成为独立的研究关注点。由此，我们的工作可在不损失精度的情况下验证聚合与推断过程。我们完整实现了所提方案并进行了评估。与现有工作相比，本方案实现了2-4倍的效率提升，且具备鲁棒性可广泛部署应用。