Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning

arXiv:2404.05828v1 Announce Type: cross
Abstract: In the era of cloud computing and data-driven applications, it is crucial to protect sensitive information to maintain data privacy, ensuring truly reliable systems. As a result, preserving privacy in deep learning systems has become a critical concern. Existing methods for privacy preservation rely on image encryption or perceptual transformation approaches. However, they often suffer from reduced task performance and high computational costs. To address these challenges, we propose a novel Privacy-Preserving framework that uses …

applications arxiv cloud cloud computing computing critical cs.cr cs.cv data data-driven data privacy deep learning eess.iv information operators preservation privacy protect result sensitive sensitive information systems task