PEvaChain: Privacy-preserving ridge regression-based credit evaluation system using Hyperledger Fabric blockchain.

Secure, compliant and authentic multiparty data sharing and collaborative modelling are of great significance to the accuracy of credit evaluation systems. Homomorphic encryption has the feature of supporting ciphertext calculation without sacrificing the accuracy of the model. However, the credibility and security of the existing centralized data homomorphic encryption sharing-aggregation mode have brought great hidden dangers and exacerbated the risk of private data being disclosed. Ensuring the authenticity and controllability of data are also difficulties faced by homomorphic encryption technology. To solve these problems, we propose a novel decentralized privacy-preserving credit evaluation system with trustworthy data content and calculations named PEvaChain based on Hyperledger Fabric blockchain. The PEvaChain consists of three main components: identity management, off-chain encrypted data uploading, and on-chain data security sharing-aggregation. With the help of Hyperledger Fabric's special member access mechanism, incorporating the ciphertext-policy attribute-based encryption (CP-ABE) access control scheme avoids unauthorized access. The original data are transformed by invertible random matrices off the chain, which meets data transfer agreements requirements when data uploading and eliminates the privacy disclosure concerns of data providers to a certain extent. Paillier homomorphic encryption-based data security sharing-aggregation on the chain ensures the security of multiparty data sharing and aggregation while realizing the minimum output and utilization of the original data. Security analysis demonstrates the security and compliance of PEvaChain in terms of data access, encrypted data uploading, sharing-aggregation, and storage. The experimental results show that the proposed approach is feasible, safe and efficient. • Novel privacy-preserving credit evaluation system based on Hyperledger Fabric is built. • A secure, compliant and explainable multiparty data sharing-aggregation method is proposed. • Sharing-aggregation results on-chain realize the trustworthiness in data and calculation. • Invertible random matrices-based data transformation replaces direct sharing of original data. • Paillier homomorphic encryption ensures the accuracy of the credit evaluation model. [ABSTRACT FROM AUTHOR]