Quantum-Secure Homomorphic Encryption for Privacy-Preserving Data Analytics in Financial Networks

Abstract

The rapid digital transformation of financial networks has led to large-scale data sharing and collaborative analytics across banks, fintech platforms, and regulatory institutions. While homomorphic encryption (HE) enables computation over encrypted data without revealing sensitive information, most existing HE schemes rely on classical cryptographic assumptions that are vulnerable to future quantum attacks. To address this critical security gap, this paper proposes a Quantum-Secure Homomorphic Encryption (QSHE) framework for privacy-preserving data analytics in distributed financial networks. The proposed framework integrates post-quantum cryptographic primitives with fully homomorphic encryption to ensure long-term confidentiality against quantum adversaries while maintaining analytical functionality on encrypted financial data. The system supports secure operations such as encrypted statistical analysis, fraud detection, risk assessment, and compliance monitoring without exposing raw transactional records. Experimental evaluation demonstrates that the proposed QSHE framework achieves strong privacy guarantees with acceptable computational overhead, making it suitable for real-world financial applications. The results confirm that quantum-secure homomorphic encryption is a viable and future-proof solution for secure financial data analytics in the era of quantum computing.