Rademics Research Institute

Abstract

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.

Introduction

The integration of artificial intelligence (AI) with security systems has introduced significant advancements in threat detection, anomaly identification, and overall cyber defense. However, these developments have also raised concerns regarding the privacy and security of sensitive data processed by AI systems. The need for secure AI systems capable of safeguarding confidential information while performing complex computations has never been greater. Traditional cryptographic methods, though effective in securing data, often impose significant computational overhead, making them inefficient for real-time applications. In this context, two emerging technologies Secure Multi-Party Computation (SMPC) and neural cryptographic protocols offer promising solutions to address these challenges. SMPC allows multiple parties to jointly compute functions over their inputs without revealing their individual data, while neural cryptographic protocols leverage machine learning to enhance encryption techniques, providing dynamic, adaptable security mechanisms for modern AI applications.

SMPC, as a privacy-preserving protocol, ensures that computations on sensitive data can be performed in a collaborative yet secure manner. This method prevents the leakage of private information during computation by distributing the task across multiple parties, each of which only has access to a fraction of the data. While SMPC guarantees data privacy, it often results in high computational costs due to the communication overhead and the need for complex encryption schemes. On the other hand, neural cryptographic protocols combine cryptography with artificial intelligence, utilizing deep learning techniques to generate encryption functions that adapt based on data and threat models. This ability to evolve encryption schemes offers a more flexible and efficient approach to securing data, particularly in dynamic environments where threats are constantly changing.