Credit risk: A new privacy-preserving decentralized credit assessment model

•This study proposes a new collaborative credit assessment model.•A random reversible matrix is used to enable safe multi-party data sharing for collaborative credit assessment modeling.•Random reversible matrix encryption fuses and obscures data.•The collaborative modelling approach uses multi-source data for credit assessment while protecting privacy, improving model accuracy.•Simulations prove the collaborative credit assessment model is safe and effective. To facilitate the collaborative modelling of privacy-preserving credit assessments under multi-party data sharing, this study suggests a privacy-preserving method for the collaborative modelling of linear regression credit assessments that is based on random invertible matrix transformation. The results indicate that the proposed method is capable of effectively protecting the original data information from being compromised during the collaborative modelling process. Besides, our model can integrate multidimensional data to alter the data distribution by employing random invertible matrix transformation. Finally, our collaborative modelling method can incorporate multi-party data to train a linear regression credit assessment model while maintaining data privacy.