The low-cost, accessing flexibility, agility, and mobility of cloud infrastructures have attracted medical organizations to store their high-resolution data in encrypted form. Besides storage, these infrastructures provide various image processing services for plain (non-encrypted) images. Meanwhile, the privacy and security of uploaded data depend upon the reliability of the service provider(s). The enforcement of laws towards privacy policies in health-care organizations, for not disclosing their patient’s sensitive and private medical information, restrict them to utilize these services. To address these privacy concerns for melanoma detection, we propose CryptoLesion , a privacy-preserving model for segmenting lesion region using whale optimization algorithm (WOA) over the cloud in the encrypted domain (ED). The user’s image is encrypted using a permutation ordered binary number system and a random stumble matrix. The task of segmentation is accomplished by dividing an encrypted image into a pre-defined number of clusters whose optimal centroids are obtained by WOA in ED, followed by the assignment of each pixel of an encrypted image to the unique centroid. The qualitative and quantitative analysis of CryptoLesion is evaluated over publicly available datasets provided in The International Skin Imaging Collaboration Challenges in 2016, 2017, 2018, and PH 2 dataset. The segmented results obtained by CryptoLesion are found to be comparable with the winners of respective challenges. CryptoLesion is proved to be secure from a probabilistic viewpoint and various cryptographic attacks. To the best of our knowledge, CryptoLesion is first moving towards the direction of lesion segmentation in ED.

中文翻译：

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加密病变

云基础设施的低成本、访问灵活性、敏捷性和移动性吸引了医疗组织以加密形式存储其高分辨率数据。除了存储之外，这些基础设施还为普通（非加密）图像提供各种图像处理服务。同时，上传数据的隐私和安全性取决于服务提供商的可靠性。卫生保健组织对隐私政策的执法，因为不披露患者的敏感和私人医疗信息，限制了他们使用这些服务。为了解决黑色素瘤检测的这些隐私问题，我们建议加密病变，一种隐私保护模型，用于在加密域 (ED) 中的云上使用鲸鱼优化算法 (WOA) 分割病变区域。用户的图像使用置换有序二进制数系统和随机错误矩阵进行加密。分割任务是通过将加密图像划分为预定义数量的簇来完成的，这些簇的最佳质心由 ED 中的 WOA 获得，然后将加密图像的每个像素分配给唯一的质心。定性和定量分析加密病变在提供的公开可用数据集上进行评估国际皮肤成像合作2016、2017、2018 和 PH 中的挑战2数据集。得到的分割结果加密病变被发现与各自挑战的获胜者相当。加密病变从概率的角度和各种密码攻击证明是安全的。据我们所知，加密病变首先是朝着 ED 中病灶分割的方向发展。