Homomorphic encryption-based secure multi-party computation for privacy-preserving toll revenue analytics
Sarath Babu Gosipathala · World Journal of Advanced Research and Reviews · 2024
This research introduces a homomorphic encryption-based secure multi-party computation framework that enables privacy-preserving toll revenue analytics across multiple toll operators while maintaining complete data confidentiality. The proposed Secure Toll Analytics System (STAS) allows multiple toll authorities to collaboratively analyze traffic patterns, revenue trends, and operational efficiency without revealing sensitive financial or operational data to competitors or external parties. Our methodology combines fully homomorphic encryption with novel approximation techniques to make encrypted analytics computationally feasible for large-scale toll operations involving millions of transactions daily. The system supports complex analytical operations including revenue forecasting, traffic pattern analysis, and comparative performance assessments while maintaining cryptographic security guarantees. We introduce a distributed encrypted computation protocol that enables secure collaborative analytics across toll operators without compromising competitive advantages or sensitive business information. The framework achieves remarkable performance with encrypted analytics operations completing within practical time constraints while providing provable security against honest-but-curious adversaries. Our implementation includes optimized encrypted aggregation functions, secure revenue sharing calculations, and privacy-preserving benchmarking capabilities. Experimental validation using real toll revenue data from multiple operators demonstrates the system's capability to provide valuable business insights while maintaining strict privacy requirements. The solution addresses critical needs for industry collaboration and regulatory reporting while protecting proprietary operational data.