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

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: 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 experiments demonstrate that our framework effectively preserves the privacy of medical images and maintains the diagnostic accuracy of DL models at a relatively high level, proving its effectiveness across various datasets and DL-based models.