  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
