"Encrypted AI Techniques for Anomaly Detection"

Abstract

The integration of artificial intelligence (AI) with encryption techniques has emerged as a pivotal area in enhancing data security and anomaly detection capabilities. This paper explores the convergence of encrypted AI techniques for anomaly detection, addressing the dual challenge of maintaining data privacy while effectively identifying anomalies in large datasets. We examine various cryptographic protocols such as homomorphic encryption and secure multiparty computation, which enable computations on encrypted data without compromising its confidentiality. Moreover, machine learning models, particularly deep learning architectures, are adapted to operate on encrypted data through techniques like functional encryption and differential privacy. These advancements not only safeguard sensitive information but also empower organizations to detect anomalies in real-time across diverse applications including cybersecurity, finance, and healthcare. By providing a comprehensive survey of encrypted AI techniques and their applications in anomaly detection, this paper aims to contribute to the ongoing discourse on secure and privacy-preserving AI solutions in data-driven environments.