Neural Cryptographic Protocols Using Secure Multi-Party Computation (SMPC) for Encrypted Data Processing in AI-Driven Security System
Shivi Dixit, A Ramamoorthy, K Anusha · 2025
The integration of Secure Multi-Party Computation (SMPC) and neural cryptographic protocols presents a novel approach to safeguarding data privacy in AI-driven security systems. This chapter explores the synergistic potential of these two advanced technologies, focusing on their application in privacy-preserving computations for real-time, large-scale AI systems. SMPC ensures secure collaborative computation across multiple parties without revealing sensitive data, while neural cryptographic protocols leverage machine learning to generate adaptable and efficient encryption schemes. The chapter delves into the theoretical foundations of both protocols, examines their performance benchmarks, and highlights the challenges and opportunities of combining them in AI environments. Key topics include optimizing computational efficiency, addressing scalability issues, and enhancing adversarial resilience in encrypted AI systems. By investigating the practical implications and use cases in domains such as secure federated learning, anomaly detection, and secure cloud-based AI applications, this chapter provides a comprehensive analysis of the future potential of SMPC and neural cryptography in advancing secure AI technologies. The findings offer valuable insights into developing scalable, efficient, and robust privacy-preserving solutions for next-generation AI-driven security systems.