Neural Network-Driven Privacy-Preserving Credit Risk Analysis: A Homomorphic Encryption Approach

Abstract

With the increasing importance of credit risk analysis (CRA) with an emphasis on privacy, there's a notable need for a privacy-preserving machine learning (PPML) system. To address this demand, we propose a framework presenting a novel approach to privacy-preserving credit risk analysis (PPCRA) through integrating neural networks (NN) with homomorphic encryption (HE). The proposed framework offers robust privacy protection while maintaining the efficiency and accuracy of credit risk prediction systems. The implementation utilizes libraries such as TenSEAL and Torch to develop a HE-enabled NN model capable of processing encrypted data. Comprehensive security analysis establishes resilience against numerous privacy attacks of the system and empirical validation through experiments conducted on real-world financial datasets from multiple countries. The evaluation of the NN's performance, both with and without privacy preservation measures, provides insights into the efficacy of the proposed approach. This study offers significant advancements in privacy-preserving techniques for CRA, with implications for financial institutions and data security practitioners.