A wide range of data mining applications benefit from the low latency offered by edge computing. However, edge computing suffers from limited computing resources, which inhibits the applications of the computationally expensive data mining methods. In the edge-cloud environment, usually, the participants turn to collaboratively train machine-learning models that yield more accurate prediction results. However, data owners may not be willing to sharing the own data for the privacy concerns. To handle such disparate goals, we focus on tree-based distributed data mining scheme with differential privacy, which is computationally friendly. The basic idea of our approach is based on a distributed ensemble strategy. Each participant builds an elegant decision model based on their own data, which has a good tradeoff between the computation and the accuracy of the data distribution, and shares it with other participants after being injected with the elaborate noise. Then the useful knowledge transferred from the decision models is acquired by other participants in an adaptive ensemble strategy. Both the theoretical analysis and the experiments show that our scheme provides an efficient data mining manner that can achieve a good prediction accuracy while providing rigorous privacy guarantee over the distributed data.

中文翻译：

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边缘计算的高效差分私有分布式数据挖掘方案

边缘计算提供的低延迟使各种数据挖掘应用程序受益。然而，边缘计算遭受有限的计算资源的困扰，这限制了计算上昂贵的数据挖掘方法的应用。通常，在边缘云环境中，参与者转向协作训练机器学习模型，以产生更准确的预测结果。但是，出于隐私考虑，数据所有者可能不愿意共享自己的数据。为了处理这些不同的目标，我们专注于具有差异性隐私的基于树的分布式数据挖掘方案，该方案具有计算友好性。我们方法的基本思想是基于分布式集成策略。每个参与者都根据自己的数据建立一个优雅的决策模型，在计算和数据分布的准确性之间进行了很好的权衡，并在注入精心制作的噪声后与其他参与者共享。然后，其他参与者可以从自适应模型中获取从决策模型转移来的有用知识。理论分析和实验均表明，我们的方案提供了一种有效的数据挖掘方式，可以实现良好的预测精度，同时为分布式数据提供严格的隐私保证。