This study aims to process the private medical data over eHealth cloud platform. The current pandemic situation, caused by Covid19 has made us to realize the importance of automatic remotely operated independent services, such as cloud. However, the cloud servers are developed and maintained by third parties, and may access user’s data for certain benefits. Considering these problems, we propose a specialized method such that the patient’s rights and changes in medical treatment can be preserved. The problem arising due to Melanoma skin cancer is carefully considered and a privacy-preserving cloud-based approach is proposed to achieve effective skin lesion segmentation. The work is accomplished by the development of a Z -score-based local color correction method to differentiate image pixels from ambiguity, resulting the segmentation quality to be highly improved. On the other hand, the privacy is assured by partially order homomorphic Permutation Ordered Binary (POB) number system and image permutation. Experiments are performed over publicly available images from the ISIC 2016 and 2017 challenges, as well as PH dataset, where the proposed approach is found to achieve significant results over the encrypted images (known as encrypted domain), as compared to the existing schemes in the plain domain (unencrypted images). We also compare the results with the winners of the ISBI 2016 and 2017 challenges, and show that the proposed approach achieves a very close result with them, even after processing test images in the encrypted domain. Security of the proposed approach is analyzed using a challenge-response game model.

中文翻译：

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-基于分数的安全生物医学模型，用于在 eHealth Cloud 上进行有效的皮肤病变分割

本研究旨在通过 eHealth 云平台处理私人医疗数据。当前由Covid19引起的大流行情况使我们意识到自动远程操作的独立服务（例如云）的重要性。但是，云服务器是由第三方开发和维护的，并且可能会访问用户的数据以获得某些利益。考虑到这些问题，我们提出了一种专门的方法，可以保护患者的权利和医疗变化。仔细考虑了由黑色素瘤皮肤癌引起的问题，并提出了一种基于隐私保护的云方法来实现有效的皮肤病变分割。这项工作是通过开发一个Z- 基于分数的局部颜色校正方法，将图像像素与模糊度区分开来，从而大大提高分割质量。另一方面，通过偏序同态保证隐私排列有序二进制 (POB)数系统和图像排列。对来自 ISIC 2016 和 2017 挑战的公开图像以及 PH 进行了实验 数据集，与普通域中的现有方案（未加密图像）相比，发现所提出的方法在加密图像（称为加密域）上取得了显着的结果。我们还将结果与 ISBI 2016 和 2017 挑战赛的获胜者进行了比较，并表明即使在加密域中处理测试图像之后，所提出的方法也与他们取得了非常接近的结果。使用挑战-响应博弈模型分析所提出方法的安全性。