Alipay (hangzhou) Information Technology Co., Ltd.

Ant Blockchain Technology (shanghai) Co., Ltd.

Yingting Liu

Jun Zhou

Li Wang

Chaochao Chen

One or more embodiments of the present specification provide privacy protection-based multicollinearity detection methods, apparatuses, and systems. Data alignment is performed by a member device on respective local feature data with other member devices to construct a joint feature matrix. Privacy protection-based multi-party matrix multiplication computation is performed to compute a product matrix of a transposed matrix of the joint feature matrix and the joint feature matrix. An inverse matrix of the product matrix is determined based on respective submatrices of the product matrix. A variance inflation factor of each attribute feature is determined by the member device with the other member devices using respective submatrices of the inverse matrix and the respective local feature data. Multicollinearity is determined by the member device with the other member devices based on fragment data of the variance inflation factor of each attribute feature.

Privacy protection-based multicollinearity detection methods, apparatuses, and systems

One or more embodiments of the present specification relate to a method and system for obtaining a jointly trained model based on privacy protection. An example method includes jointly training a first model, by a first device and with a second device, the first device and the second device each holding respective training data that includes first training data with a sample label, and second training data without the sample label. Jointly training the first model includes privately obtaining the first training data, and performing joint model training using the first training data. The second training data is input to the trained first model to obtain a predicted label for the second training data. Jointly training a second model includes privately obtaining labeled training data, and performing joint model training using the first training data, the second training data, and the labeled training data.

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Hangzhou, China

Yingting Liu