Privacy-Preserving Data Obfuscation for Credit Scoring

In this work, we present a privacy-preserving framework for credit scoring systems deployed on Machine Learning as a Service (MLaaS) platforms. Our approach integrates an obfuscator-classifier model that enhances privacy while maintaining high accuracy for loan default prediction tasks. The obfuscator transforms sensitive financial data into a privacy-protected representation, minimizing the risk of privacy leakage and input reconstruction during inference. By employing a combination of center loss and noise addition, our model ensures a robust balance between privacy and utility. Through extensive experiments, we demonstrate the effectiveness of our solution in reducing information leakage. For instance, our method achieves a 95.05% reduction in the average R2 score of reconstruction attacks, from 0.921 to 0.045. At the same time, we maintain high prediction accuracy, with only a negligible loss of 1.06% in public task accuracy, despite the added noise. These results highlight the scalability and adaptability of our framework for financial MLaaS applications, providing strong privacy protection without significantly compromising model performance.