  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: Image Hidden Module (IHM) and Image Quality Enhancement
Module~(IQEM). The~IHM performs in the frequency domain, blending the features
of plaintext medical images into host medical images, and then combines this
with IQEM to improve and create surrogate images effectively. During the
diagnostic model training process, only surrogate images are used, enabling
anonymous analysis without any plaintext data during both training and
inference stages. Extensive evaluations demonstrate that our framework
effectively preserves the privacy of medical images and maintains diagnostic
accuracy of DL models at a relatively high level, proving its effectiveness
across various datasets and DL-based models.
