Abstract: Multi source heterogeneous data may contain sensitive and personal privacy information, increasing the risk of data leakage. Therefore, a multi-source heterogeneous data security aggregation algorithm based on kernel extreme learning machine is designed. Partial least squares algorithm is used to extract features from multi-source heterogeneous data, the extreme learning machine is optimized by introducing kernel functions, and the obtained data features inputtied into the kernel extreme learning machine to complete data aggregation by class. Elliptic curve encryption algorithm is used to encrypt the aggregated data, improving data security and achieving the goal of secure aggregation of multi-source heterogeneous data. The experimental results show that the algorithm has high accuracy in multi-source heterogeneous data aggregation and good data encryption performance, and can be widely applied in practice. 

Key words: kernel extreme learning machine, multi source heterogeneous data, data security aggregation, partial least squares algorithm, elliptic curve encryption