Privacy-Preserving Deep Learning Using Deformable Operators for Secure
  Task Learning
Fabian Perez Jhon Lopez Henry Arguello
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 a set of
deformable operators for secure task learning. Our method involves shuffling
pixels during the analog-to-digital conversion process to generate visually
protected data. Those are then fed into a well-known network enhanced with
deformable operators. Using our approach, users can achieve equivalent
performance to original images without additional training using a secret key.
Moreover, our method enables access control against unauthorized users.
Experimental results demonstrate the efficacy of our approach, showcasing its
potential in cloud-based scenarios and privacy-sensitive applications.