Federated Threat Intelligence Exchange Protocol (F-TIXP): Privacy-Preserving Collaborative Cyber Defense Framework

Abstract

Cyber threats increasingly exploit the silos separating organizational defenses. Current threat-intelligence sharing frameworks (STIX/TAXII, MISP, OpenCTI) remain reactive, centralized, and trust-based, exposing sensitive telemetry and creating single points of failure. As nation-state campaigns and polymorphic malware evolve, critical sectors such as finance, healthcare, and government require a collaborative yet privacy-preserving intelligence fabric.

This study introduces the Federated Threat Intelligence Exchange Protocol (F-TIXP)—a global, multi-sector framework that enables autonomous, encrypted, real-time intelligence sharing without revealing proprietary data. Its central innovation, the Federated Cognitive Exchange Grid (FCEG), employs federated learning, homomorphic encryption, and differential privacy to exchange model insights instead of raw indicators. Each organization trains local detection models on internal telemetry; only encrypted parameter updates are exchanged through the F-TIXP mesh. A decentralized reputation mechanism validates contributors and continuously adjusts trust scores among participants.

Prototype evaluation across simulated finance, healthcare, and government networks demonstrated 48 % faster detection of novel attack campaigns, 62 % reduction in false positives, and sub-second synchronization latency across 50 federated nodes. F-TIXP transforms cyber-defense collaboration from static data feeds into a living, self-learning ecosystem resilient to data-exfiltration risks and regulatory boundaries.