Enhancing Credit Card Fraud Detection: Privacy-Preserving Association Rule Mining with Secure Multi-Party Computation

The proposed research presents a novel method of enhancing credit-card fraud detection using Secure Multi-Party Computation (SMPC), a privacy-saving method. At present data privacy is of utmost concern and the proposed methodology can eliminate the problem of carrying out fruitful fraud detection without putting privacy of user data in danger. Using SMPC, several entities are able to work with transaction data in a collaborative fashion without revealing any of the individual data, establishing privacy compliance. The study aims at a privacy-preserving framework for identifying fraud in credit card payments by linking SMPC and the FP-Growth algorithm. The usage of SMPC means that cooperative calculations may be done without revealing sensitive data, avoiding the possibility of data leakage. As a powerful Association Rule Mining (ARM) approach, FP-Growth protects the secrecy of individual transactions while effectively detecting hidden patterns and correlations inside transaction datasets. The framework is tested using extensive performance metrics including as accuracy, precision, recall, and F1-score, which serve as a robust standard for determining its dependability. The results show that the suggested technique is highly effective in identifying fraudulent actions while ensuring tight privacy protection.