The Latency-Privacy Paradox: A Review of Cryptographic Data Mining in Financial Intrusion Detection

Abstract

Mandatory end-to-end encryption across contemporary banking infrastructures essentially blinds legacy intrusion detection mechanisms. This review unpacks the ontological collision between applied cryptography and network data mining—a theoretical intersection currently paralyzed by an unresolved latency-privacy bottleneck. By systematically synthesizing recent literature on hybrid Intrusion Detection Systems (IDS), we map a highly fragmented academic landscape. Solutions that enforce absolute mathematical privacy via Fully Homomorphic Encryption (FHE) collapse under their own computational burden—often introducing latency overheads of up to six orders of magnitude—during high-frequency trading (HFT) simulations. Alternatively, heuristic metadata mining bypasses decryption entirely but is highly fragile to adversarial spoofing and generative traffic manipulation. Evaluating these disparate trajectories reveals a critical gap: existing frameworks rarely account for the strict microsecond tolerances required by institutional trading floors. Ultimately, this review argues that feature-level Order-Preserving Encryption (OPE) combined with gradient-boosted classifiers provides a highly operationally viable compromise. This paper concludes by identifying promising future research directions in targeted obfuscation and urges a pivot away from mathematically flawless encryption toward hardware-accelerated, latency-aware detection topologies that secure proprietary trading signals without sacrificing line-speed threat mitigation.