Privacy-Preserving Model for Cyber Threat Intelligence, Sharing Across Multi-Organizational Platforms

The research presents a privacy-preserving structure for secure Cyber Threat Intelligence (CTI) exchanging, using cutting-edge AI techniques to increase cooperative IT delicacy while keeping secret information safe. We propose a system based on Federated Learning, Differential Privacy and Homomorphic Encryption to model training in multiple organizations without exchanging the raw data. Using these privacy-preserving techniques together, organizations can collaborate to enhance their overall threat detection and response capabilities while maintaining the confidentiality of sensitive data. The implemented results indicate significant advancements with a 50%-55% detection accuracy overall for role intention measurement, model accuracy increase to 82% (predicting the next role intention), 30% computational overhead improvement, and a 10% improvement in novel threat detection. It provides 85% compliance against regulatory standards including GDPR and NIST. The applicability of the proposed solution on real-world data in the financial and healthcare domains demonstrates the proposed solution achieves a balance between privacy protection and CTI sharing still providing a powerful method for distributed, scalable and efficient collaboration in secure cybersecurity .