"Encrypted AI for Remote Sensing Data Analysis"

Abstract

Remote sensing data analysis has become a critical component in various fields such as environmental monitoring, urban planning, and disaster management. However, the sensitive nature of the data necessitates robust security measures to protect against unauthorized access and ensure privacy. This paper explores the integration of encrypted artificial intelligence (AI) techniques into remote sensing data analysis to address these security concerns. We propose a novel framework that employs homomorphic encryption to enable AI models to perform computations on encrypted data without the need for decryption. This ensures that data remains secure throughout the processing pipeline. Our approach leverages advanced machine learning algorithms tailored for remote sensing applications, such as convolutional neural networks (CNNs) and support vector machines (SVMs), adapted to operate within an encrypted domain. Experimental results demonstrate that the proposed encrypted AI framework achieves competitive performance compared to traditional methods while maintaining data confidentiality. This work paves the way for secure, efficient, and scalable remote sensing data analysis, fostering trust and enabling broader adoption in security-sensitive applications.