Privacy-Preserving Cryptography for Credit Card Reward Systems: A Secure Multi-Party Computation Approach

Abstract

This article presents a comprehensive framework for implementing privacy-preserving credit card reward systems using Secure Multi-Party Computation (SMPC) technologies. Traditional reward architectures require extensive sharing of sensitive transaction data across multiple entities, creating significant privacy risks, security vulnerabilities, and regulatory compliance challenges. It leverages cryptographic advances to enable card issuers, payment networks, and merchant partners to collaborate on reward calculations,fraud detection, and personalized offers without revealing sensitive transaction details to one another. The article explores the evolution of privacy-preserving technologies in financial systems, comparing Fully Homomorphic Encryption, Zero-Knowledge Proofs, and SMPC approaches. A detailed case study of a travel rewards program implementation demonstrates how this framework ensures data remains protected throughout the entire process while maintaining the performance characteristics necessary for production deployment. The system provides comprehensive privacy protection, enhances fraud detection capabilities through secure collaboration, and facilitates compliance with evolving privacy regulations.Performance evaluations confirm the practical viability of the article, with minimal latency impact, strong scalability characteristics, and robust security guarantees. It contributes to the growing field of privacy-enhancing technologies for financial services and offers a viable solution to balance analytical utility with privacy protection in consumer-facing applications.