Securing AI Models: Cryptographic Approaches to Protect AI Algorithms and Data

Abstract

This paper addresses the growing necessity of protecting AI models and their underlying data using cryptographic techniques. As AI continues to integrate into critical industries such as healthcare, finance, and autonomous systems, unique vulnerabilities arise that expose models to threats like data poisoning, adversarial attacks, and model inversion. By applying cryptographic methods like homomorphic encryption, differential privacy, and secure multiparty computation, organizations can safeguard both the integrity and confidentiality of AI models and training data. This white paper provides insights into these cryptographic approaches, detailing how each can protect against specific threats while maintaining model performance and compliance.