Privacy-Preserving AI Co-Defense using Secure Multi-Party Computation in Distributed Cloud Environments

Abstract

As cyber threats grow increasingly sophisticated, collaborative AI-driven defense has become essential. Yet sharing threat intelligence across organizations conflicts with data privacy regulations (GDPR, CCPA) and competitive interests. This paper presents Privacy-Preserving AI Co-Defense (PPAICD): a comprehensive architecture integrating Secure Multi-Party Computation (SMPC) with Federated Learning in distributed cloud environments. Participating organizations jointly train AI threat-detection models and aggregate intelligence without exposing individual datasets. We formalize the security model, present a three-layer system architecture, analyze adversarial threats including Byzantine fault tolerance and gradient inversion, integrate differential privacy guarantees, and propose hardware-acceleration pathways. We provide a comparative evaluation against existing SMPC-based systems, evaluate Byzantine-resilient aggregation under four realistic adversarial scenarios, quantify differential privacy noise impact on threat detection accuracy, and report results from a three-organization proof-of-concept deployment. Performance analysis demonstrates that SMPC-based co-defense is cryptographically principled, legally compliant, and approaching practical real-time deployability.