Exploiting Semi-Tensor Product Compressed Sensing and Hybrid Cloud for Secure Medical Image Transmission

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Abstract
With the development of telemedicine diagnosis technology, the collection, storage, and transmission of medical data has become a pivotal problem. To solve these problems, in terms of semi-tensor product compressed sensing (STP-CS) and hybrid cloud, a new medical data transmission framework is presented in this article, which can ensure the efficiency, confidentiality, and verifiability of data transmission. According to the edge detection, the authentication information is embedded into the insignificant area of the medical image to protect the authenticity of the data. STP-CS is utilized to measure and encrypt the medical image, which improves the efficiency of data transmission. In order to make the image data more secure, an adaptive cyclic shift diffusion algorithm based on chaotic sequence is used to effectively diffuse the measurement results. In addition, we also apply encoding for the quantized measurement value to be used as tamper proof authentication. The simulation results prove that the presented medical data transmission scheme can effectively improve the image reconstruction effect, transmission efficiency, and security.
Funding Information
  • Key Research and Development and Promotion Special Project of Henan Province (202102210380)
  • Postgraduate Education Reform and Quality Improvement Project of Henan Province (YJS2022JD26)
  • Open Foundation of Henan Key Laboratory of Cyberspace Situation Awareness (HNTS2022019)
  • Pre-research project of SongShan Laboratory (YJC012022011)
  • Guangxi Key Laboratory of Trusted Software (KX202027)
  • Postgraduate Education Innovation Training Base (SYLJD2022008)

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