Credit risk: A new privacy-preserving decentralized credit assessment model
Xianhua Kuang,
Chaoqun Ma and
Yi-Shuai Ren
Finance Research Letters, 2024, vol. 67, issue PB
Abstract:
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.
Keywords: Credit assessment; Privacy protection; Data privacy; Linear regression (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S154461232400967X
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:67:y:2024:i:pb:s154461232400967x
DOI: 10.1016/j.frl.2024.105937
Access Statistics for this article
Finance Research Letters is currently edited by R. Gençay
More articles in Finance Research Letters from Elsevier
Bibliographic data for series maintained by Catherine Liu ().