With the rise of the Internet of Things (IoT) and fifth‐generation (5G) networks, which have led to a surge in data processing and increased data transfer time, traditional cloud computing could no longer meet the needs of workers, so edge computing has emerged. Edge computing could meet the demand for low time consumption by processing data at the edge of the network and then transmitting it to a third‐party platform. However, since the credibility of the third‐party platform is unknown which can easily leak the privacy of workers. For the transparent mechanism of blockchain, a two‐stage privacy protection mechanism based on blockchain is proposed to solve this problem. In the first stage, this paper proposes a double disturbance localized differential privacy (DDLDP) algorithm to disturb the location information of workers. In the second stage, all the sensing data are uploaded to the blockchain through edge nodes, processed by the edge cloud, and fed back to the requester. Blockchain technology not only guarantees the integrity of sensing data, but also prevents the possibility of third‐party platforms from leaking workers' privacy. Through extensive performance evaluation and comparative experiments on real data sets, the DDLDP algorithm could effectively protect the privacy of workers and has higher service quality and data availability.

中文翻译：

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移动众包中基于区块链的两阶段隐私保护机制

随着物联网（IoT）和第五代（5G）网络的兴起，导致数据处理激增和数据传输时间增加，传统的云计算不再能够满足员工的需求，因此边缘计算已经出现。边缘计算可以通过在网络边缘处理数据，然后将其传输到第三方平台来满足低耗时的需求。但是，由于第三方平台的信誉未知，因此很容易泄露工作人员的隐私。针对区块链的透明机制，提出了一种基于区块链的两阶段隐私保护机制来解决这一问题。在第一阶段，本文提出了一种双重干扰局部差分隐私（DDLDP）算法来干扰工人的位置信息。在第二阶段 所有传感数据都通过边缘节点上传到区块链，由边缘云进行处理，然后反馈给请求者。区块链技术不仅可以保证传感数据的完整性，而且还可以防止第三方平台泄露工作人员隐私的可能性。通过广泛的性能评估和对真实数据集的对比实验，DDLDP算法可以有效地保护工作人员的隐私，并具有更高的服务质量和数据可用性。