"Challenges in Scaling Encrypted AI to Large Datasets"

Abstract

As the demand for privacy-preserving artificial intelligence (AI) grows, encrypted AI techniques have emerged as promising solutions to safeguard sensitive data while enabling meaningful analysis. However, scaling these techniques to handle large datasets poses significant challenges. This abstract explores the primary obstacles faced in scaling encrypted AI to large datasets, focusing on computational complexity, communication overhead, and the trade-offs between security and performance. It discusses current approaches, such as homomorphic encryption and secure multiparty computation, highlighting their strengths and limitations in large-scale applications. Furthermore, it examines potential avenues for future research and development to mitigate these challenges and advance the adoption of encrypted AI in handling massive datasets securely and efficiently.