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Mining the spatial and temporal correlation of wind farm output data isbeneficial for enhancing the precision of ultra-short-term wind powerprediction. However, if the wind farms are owned by separate entities, they maybe reluctant to share their data directly due to privacy concerns as well asbusiness management regulation policies. Although cryptographic approaches havebeen designed to protect privacy in the process of data sharing, it is still achallenging problem to encrypt the original data while extracting the nonlinearrelationship among multiple wind farms in the machine learning process. Thispaper presents pwXGBoost, a technique based on the machine learning tree modeland secure multi-party computation (SMPC) that can successfully extractcomplicated relationships while preserving data privacy. A maximum meandiscrepancy (MMD) based scheme is proposed to effectively choose adjacentcandidate 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 clusterof wind farms in Inner Mongolia, China, demonstrating that it is capable ofachieving considerable efficiency and performance improvements while preservingprivacy

Privacy Preserving Ultra-Short-term Wind Power Prediction Based on Secure Multi Party Computation