"Encrypted AI for Real-time Video Analytics"

Abstract

In recent years, the proliferation of surveillance systems and the need for privacy-preserving techniques have driven advancements in encrypted artificial intelligence (AI) for real-time video analytics. This paper explores the intersection of encryption techniques and AI algorithms to enable secure and efficient processing of video data while preserving individual privacy. Key challenges in this domain include maintaining high computational efficiency and accuracy while ensuring robust encryption of sensitive video content. This study reviews various encryption methodologies such as homomorphic encryption, secure multiparty computation, and differential privacy, evaluating their applicability and performance in real-time video analytics scenarios. Additionally, the paper discusses practical implementations and case studies where encrypted AI techniques have been successfully deployed, highlighting their effectiveness in addressing privacy concerns without compromising analytical insights derived from video data. Ultimately, this research contributes to the evolving landscape of AI-driven video analytics by presenting a framework for integrating encryption with AI to achieve both security and analytical efficacy in real-time applications.