Heterogeneous Privacy-Preserving Blockchain-Enabled Federated Learning for Social Fintech

Social fintech integrates financial technology with social networking to enhance financial services’ accessibility and personalization by leveraging social interactions and user data. This approach raises privacy security concerns, particularly in application based on centralized artificial intelligence systems. To address these issues, blockchain-enabled federated learning (BEFL) offers a decentralized solution, improving robustness and privacy but facing challenges such as privacy attacks and heterogeneous crypto system. In response, a novel PKI and identity-based heterogeneous authenticated asymmetric group key agreement (PKI-IB-HAAGKA) protocol was proposed, which resolves crypto system heterogeneity issues. What's more, a PKI and identity-based heterogeneous batch multisignature (PKI-IB-HBMS) was proposed as a building block of PKI-IB-HAAGKA. This article presents the heterogeneous privacy-preserving blockchain-enabled federated learning (HPP-BEFL) system, designed to enhance privacy, security, and efficiency in social fintech applications. It effectively mitigates man-in-the-middle and inference attacks while improving overall system performance. Through security analysis and experiment results, it is demonstrated that the proposed PKI-IB-HAAGKA, PKI-IB-HBMS, and HPP-BEFL are provably secure and highly efficient, which can be applied to large-scale heterogeneous privacy-preserving model training scenarios.