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An efficient scheme for secure domain medical image fusion over cloud

机译:一种通过云实现安全域医学图像融合的有效方案

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摘要

The exponential growth in the medical images is making the healthcare industry move towards cloud-based paradigm, which has vast storage and high end processing facilities. However, moving medical images containing highly sensitive data to third-party cloud servers brings in serious security threats. Even though encrypting medical images before outsourcing using traditional encryption schemes seem to be a feasible solution, that can not support encrypted domain processing. In this paper, we propose an affine Hill cipher based scheme for encrypted domain medical image fusion. The random vectors used in this scheme are carefully designed to preserve the randomness and security properties when operations are performed on the encrypted data. The proposed scheme offers data privacy and supports encrypted domain processing with no additional storage burden at the cloud side and very low computational burden at the healthcare provider side. The security of the proposed scheme is evaluated through extensive cryptanalysis in terms of resistance against various statistical attacks. The performance of the proposed scheme is analyzed by comparing various metrics of encrypted domain MR-CT/PET image fusion results with those of plaintext domain fusion. The values of structural similarity index, normalized correlation coefficient and structural content are 1 and the image quality index is 0.999, which show that the proposed encrypted domain image fusion provides same accuracy levels as that of plaintext domain image fusion.
机译:医学图像的指数增长正在使医疗保健行业向基于云的范例发展,该范例具有庞大的存储和高端处理设施。但是,将包含高度敏感数据的医学图像移至第三方云服务器会带来严重的安全威胁。尽管使用传统的加密方案在外包之前对医学图像进行加密似乎是一种可行的解决方案,但它不支持加密域处理。在本文中,我们提出了一种基于仿射希尔密码的加密域医学图像融合方案。在此方案中使用的随机向量经过精心设计,以在对加密数据执行操作时保留随机性和安全性。所提出的方案提供了数据保密性,并支持加密域处理,而在云侧没有额外的存储负担,而在医疗保健提供者侧却具有非常低的计算负担。通过对各种统计攻击的抵抗力,通过广泛的密码分析来评估所提出方案的安全性。通过比较加密域MR-CT / PET图像融合结果与纯文本域融合的各种指标,分析了该方案的性能。结构相似度指数,归一化相关系数和结构内容的取值为1,图像质量指数为0.999,表明所提出的加密域图像融合具有与明文域图像融合相同的准确度。

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