Privacy-Preserving Deep Learning Using Deformable Operators for Secure Task Learning
This addresses privacy concerns for users in cloud computing and data-driven applications, though it appears incremental by building on existing encryption and transformation methods.
The paper tackles the problem of preserving privacy in deep learning systems by proposing a framework using deformable operators to shuffle pixels during data conversion, achieving equivalent performance to original images without extra training.
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.