"Scalability Issues in Encrypted AI Model Deployment"

Abstract

The rapid advancement in artificial intelligence (AI) and machine learning (ML) has led to the deployment of increasingly complex models in various domains. However, the integration of encryption mechanisms to secure these models introduces significant scalability challenges. This paper addresses the critical issues encountered in the deployment of encrypted AI models, focusing on computational overhead, latency, and resource utilization. We explore current encryption techniques, such as homomorphic encryption and secure multi-party computation, evaluating their impact on the performance and scalability of AI systems. Additionally, we propose potential solutions and optimizations to mitigate these challenges, ensuring robust security without compromising on the efficiency and scalability of AI deployments. Our findings are supported by empirical data and case studies, highlighting the trade-offs and considerations necessary for practical implementation. This research aims to provide a comprehensive understanding of the scalability issues in encrypted AI model deployment, offering valuable insights for researchers and practitioners in the field.