Medical data security is facing great challenges in medical applications due to the open internet and the semi-trusted cloud. For the sake of privacy protection and the security of medical images, this paper proposes a secure and high visual-quality framework for medical images. In this framework, a novel reversible data hiding (RDH) based on lesion extraction embeds privacy data into medical images for privacy protection and image quality improvement, homomorphic encryption based on chaotic map encrypts images for medical image security. The experiments have shown that the proposed framework increases the security of medical data and improves the visual quality of medical images significantly. The proposed RDH in this framework outperforms the other RDH methods with contrast enhancement and the proposed encryption scheme increases image security well. INDEX TERMSReversible data hiding, homomorphic encryption, contrast enhancement, security, medical images.

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The vehicle data currently collected from various terminal devices has the shortcomings of large amount of information, strong heterogeneity, data conflicts and other issues. The existing data fusion model has cross and not completely consistent with the characteristics and application requirements of the field of transportation, and are not suitable for the information features of this field. To solve this problem, this paper proposes a GFIC-I data fusion model and methods suitable for the applications of automotive transportation field. The model describes five key steps of data fusion. Through the fusion of real-time data and historical data in time and spatial, the integrated data is calculated, which has lower ambiguity and higher accuracy and can improve the effective uses of the data and provide professional guidance for data fusion applications.