Plaintext-Free Deep Learning for Privacy-Preserving Medical Image Analysis via Frequency Information Embedding

arXiv:2403.16473v1 Announce Type: new
Abstract: In the fast-evolving field of medical image analysis, Deep Learning (DL)-based methods have achieved tremendous success. However, these methods require plaintext data for training and inference stages, raising privacy concerns, especially in the sensitive area of medical data. To tackle these concerns, this paper proposes a novel framework that uses surrogate images for analysis, eliminating the need for plaintext images. This approach is called Frequency-domain Exchange Style Fusion (FESF). The framework includes two main components: …

analysis area arxiv cs.cr data deep learning eess.iv fast free image image analysis information medical medical data plaintext privacy privacy concerns sensitive training