Mining the spatial and temporal correlation of wind farm output data is beneficial for enhancing the precision of ultra-short-term wind power prediction. However, if the wind farms are owned by separate entities, they may be reluctant to share their data directly due to privacy concerns as well as business management regulation policies. Although cryptographic approaches have been designed to protect privacy in the process of data sharing, it is still a challenging problem to encrypt the original data while extracting the nonlinear relationship among multiple wind farms in the machine learning process. This paper presents pwXGBoost, a technique based on the machine learning tree model and secure multi-party computation (SMPC) that can successfully extract complicated relationships while preserving data privacy. A maximum mean discrepancy (MMD) based scheme is proposed to effectively choose adjacent candidate wind farms to participate in the collaborative model training, therefore improving the accuracy and reducing the burden of data acquisition. The proposed method was evaluated on real world data collected from a cluster of wind farms in Inner Mongolia, China, demonstrating that it is capable of achieving considerable efficiency and performance improvements while preserving privacy

翻译：挖掘风电场输出数据的时空相关性有助于提高超短期风电功率预测的精度。然而，若风电场由不同实体所有，由于隐私顾虑及商业管理监管政策，它们可能不愿直接共享数据。尽管已设计出密码学方法以保护数据共享过程中的隐私，但在机器学习过程中对原始数据进行加密的同时提取多个风电场间的非线性关系仍是一个具有挑战性的问题。本文提出pwXGBoost，一种基于机器学习树模型与安全多方计算（SMPC）的技术，该技术能在保护数据隐私的同时成功提取复杂关系。提出了一种基于最大均值差异（MMD）的方案，以有效选择相邻的候选风电场参与协作模型训练，从而提高预测精度并减轻数据采集负担。该方法在中国内蒙古一群风电场的实测数据上进行了评估，结果表明其在保护隐私的同时能够实现显著的效率与性能提升。