A New Data Model for the Privacy Protection of Medical Images

Abstract

Benefiting from the intelligent Medical Internet of Things (IoMT), the medical industry has dramatically improved its quality and productivity. The transmission of biomedical data in an open and untrusted network poses a new challenge to the privacy protection of patient information. The low processing power of IoMT limited the application of traditional encryption to protect sensitive data. In this paper, we developed a new data protection model for medical images. The model uses visual cryptography (VC) to store biomedical data in a separate database, which can transfer the sensitive data of patients simply and securely. To alleviate the degradation of biomedical recognition performance caused by VC-based noise, we further use transfer learning to train an optimized neural network. The experimental results show that this proposed method provides privacy in the IoMT environment and maintains the high accuracy of biomedical recognition.