Secure Training and Inference in AI: Cryptographic Perspectives

Abstract

In recent years, the integration of artificial intelligence (AI) into critical applications has raised concerns about data privacy and security. Traditional AI training and inference processes often involve handling sensitive data, making them susceptible to various attacks. Cryptographic techniques offer promising solutions to mitigate these risks by enabling secure AI operations without compromising data confidentiality. This paper explores cryptographic perspectives on securing AI training and inference, emphasizing techniques such as homomorphic encryption, secure multiparty computation, and differential privacy. We discuss their application in protecting data during both the training phase, where sensitive information is used to build AI models, and the inference phase, where model predictions are made on potentially sensitive inputs. Additionally, we examine the challenges and future directions in the intersection of AI and cryptography, aiming to provide a comprehensive overview of the state-of-the-art approaches and their implications for secure AI deployment.