Cloud computing gives clients the convenience of outsourcing data calculations. However, it also brings the risk of privacy leakage, and datasets that process industrial IoT information have a high computational cost for clients. To address these problems, this paper proposes a secure grid‐based density peaks clustering algorithm for a hybrid cloud environment. First, the client utilizes the homomorphic encryption algorithm to construct encrypted objects with client dataset. Second, the client uploads the encrypted data to the cloud servers to implement our security protocol. Finally, the cloud servers return the clustering results with the disturbance to the client. The experimental results on the UCI datasets and the smart power grid dataset reveal that the secure algorithm presented in this paper can improve upon the precision and efficiency of other clustering algorithms while also preserving user privacy. Moreover, it only performs encryption and removes the disturbance operation on the client, so that the client has lower computational complexity. Therefore, the secure clustering scheme proposed in this paper is applicable to industrial IoT big data and has high security and scalability.

中文翻译：

---

在用于工业物联网的混合云上安全地基于网格的密度峰聚类

云计算为客户提供了外包数据计算的便利。但是，这也带来了隐私泄露的风险，处理工业物联网信息的数据集对客户而言具有很高的计算成本。为了解决这些问题，本文提出了一种用于混合云环境的基于安全网格的密度峰聚类算法。首先，客户端利用同态加密算法来构建具有客户端数据集的加密对象。其次，客户端将加密的数据上传到云服务器以实施我们的安全协议。最后，云服务器将带有干扰的聚类结果返回给客户端。在UCI数据集和智能电网数据集上的实验结果表明，本文提出的安全算法可以提高其他聚类算法的精度和效率，同时还能保护用户隐私。而且，它仅执行加密并消除客户端上的干扰操作，从而客户端具有较低的计算复杂度。因此，本文提出的安全聚类方案适用于工业物联网大数据，具有较高的安全性和可扩展性。