Privacy-Preserving Credit Card Approval Using Homomorphic SVM: Toward Secure Inference in FinTech Applications

TL;DR

This paper introduces PP-FinTech, a privacy-preserving machine learning scheme using homomorphic encryption and SVM for secure credit card approval, balancing privacy and efficiency in financial applications.

Contribution

It presents a novel CKKS-based encrypted SVM with hybrid kernel and adaptive thresholding for secure, non-linear classification in finance.

Findings

Achieves comparable accuracy to plaintext models.

Demonstrates practical efficiency in encrypted inference.

Balances privacy with computational performance.

Abstract

The growing use of machine learning in cloud environments raises critical concerns about data security and privacy, especially in finance. Fully Homomorphic Encryption (FHE) offers a solution by enabling computations on encrypted data, but its high computational cost limits practicality. In this paper, we propose PP-FinTech, a privacy-preserving scheme for financial applications that employs a CKKS-based encrypted soft-margin SVM, enhanced with a hybrid kernel for modeling non-linear patterns and an adaptive thresholding mechanism for robust encrypted classification. Experiments on the Credit Card Approval dataset demonstrate comparable performance to the plaintext models, highlighting PP-FinTech's ability to balance privacy, and efficiency in secure financial ML systems.