In smart applications such as smart medical equipment, more data needs to be processed and trained locally and near the local end to prevent privacy leaks. However, the storage and computing capabilities of smart devices are limited, so some computing tasks need to be outsourced; concurrently, the prevention of malicious nodes from accessing user data during outsourcing computing is required. Therefore, this paper proposes EVPP (efficient, verifiable, and privacy-preserving), which is a computing outsourcing scheme used in the training process of machine learning models. The edge nodes outsource the complex computing process to the edge service node. First, we conducted a certain amount of testing to confirm the parts that need to be outsourced. In this solution, the computationally intensive part of the model training process is outsourced. Meanwhile, a random encryption perturbation is performed on the outsourced training matrix, and verification factors are introduced to ensure the verifiability of the results. In addition, the system can generate verifiable evidence that can be generated to build a trust mechanism when a malicious service node is found. At the same time, this paper also discusses the application of the scheme in other algorithms in order to be better applied. Through the analysis of theoretical and experimental data, it can be shown that the scheme proposed in this paper can effectively use the computing power of the equipment.

中文翻译：

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一种安全可验证的辅助移动设备训练机器学习模型的外包方案

在诸如智能医疗设备之类的智能应用程序中，需要在本地和本地端附近处理和训练更多数据，以防止隐私泄漏。但是，智能设备的存储和计算能力有限，因此一些计算任务需要外包。同时，需要防止恶意节点在外包计算期间访问用户数据。因此，本文提出了EVPP（高效，可验证和隐私保护），这是一种用于机器学习模型训练过程中的计算外包方案。边缘节点将复杂的计算过程外包给边缘服务节点。首先，我们进行了一定数量的测试以确认需要外包的零件。在此解决方案中，模型训练过程中计算量大的部分已外包。同时，对外包训练矩阵进行随机加密扰动，并引入验证因子以确保结果的可验证性。此外，系统可以生成可验证的证据，当发现恶意服务节点时，可以生成该证据以建立信任机制。同时，本文还讨论了该方案在其他算法中的应用，以便更好地应用。通过理论和实验数据的分析，可以证明本文提出的方案可以有效利用设备的计算能力。当发现恶意服务节点时，系统可以生成可验证的证据，该证据可以生成以建立信任机制。同时，本文还讨论了该方案在其他算法中的应用，以便更好地应用。通过理论和实验数据的分析，可以证明本文提出的方案可以有效利用设备的计算能力。当发现恶意服务节点时，系统可以生成可验证的证据，该证据可以生成以建立信任机制。同时，本文还讨论了该方案在其他算法中的应用，以便更好地应用。通过理论和实验数据的分析，可以证明本文提出的方案可以有效利用设备的计算能力。