Cloud computing along with the Internet of Things (IoT) is proving to be an essential tool for delivering better healthcare services. However, maintenance, privacy, confidentiality, and security of Electronic Health Information (EHI) pose a huge challenge in telemedicine. The sharing of EHI with a remote doctor over the cloud is an important issue since the minute variation may lead to the wrong diagnosis. Despite the plethora of research in this field, there is an immense necessity to develop the algorithms for enhancing security in e-healthcare systems. In this paper, an innovative Reversible Data Hiding (RDH) scheme using Lagrange’s interpolation polynomial, secret sharing, and bit substitution for EHI security has been proposed. The cover medical image is sub-sampled, into four shares. Image interpolation is used to enlarge the subsamples, for hiding EHI. The secret information is processed using Lagrange’s interpolation polynomial before being embedded in the various cover image shares. The data is embedded into the interpolated sub-sampled shares at the locations pre-defined by the algorithm. The distributive nature of embedded data enhances the security of the proposed framework while maintaining reversibility. We show that only 75% of shares are required to obtain the whole embedded data and the undistorted cover image. The proposed scheme outperforms the schemes under comparison in terms of imperceptibility and payload. It can reversibly embed 163,840 bits (0.75 bits per pixel) with an average PSNR of about 52.38 dB. The average values of relative entropy, the difference in relative entropy, standard deviation, and cross-correlation are 7.3242, 0.0382, 65.0539, and 0.9838, respectively, for the first sub-sample. It shows an increase of about 3 dB for a payload of 1, 30,000 bits when compared to the state-of-art. Further, it is pertinent to mention that the proposed scheme has lower computational complexity and is hence useful for e-healthcare applications. Given all the attributes of the scheme along with its lower computational complexity, it is suitable for EHI security in a distributive environment like cloud computing.

事实证明，云计算和物联网 (IoT) 是提供更好的医疗保健服务的重要工具。然而，电子健康信息 (EHI) 的维护、隐私、机密性和安全性对远程医疗提出了巨大挑战。通过云与远程医生共享 EHI 是一个重要问题，因为微小的变化可能会导致错误的诊断。尽管在该领域进行了大量研究，但仍然有必要开发算法以增强电子医疗保健系统的安全性。在本文中，提出了一种创新的可逆数据隐藏 (RDH) 方案，该方案使用拉格朗日插值多项式、秘密共享和位替换来实现 EHI 安全。封面医学图像被二次采样，分成四份。图像插值用于放大子样本，用于隐藏 EHI。秘密信息在嵌入各种封面图像共享之前使用拉格朗日插值多项式进行处理。数据被嵌入到由算法预先定义的位置处的内插子采样份额中。嵌入式数据的分布式特性增强了所提议框架的安全性，同时保持了可逆性。我们表明，只需要 75% 的份额即可获得整个嵌入数据和未失真的封面图像。所提出的方案在不可感知性和有效载荷方面优于比较方案。它可以可逆地嵌入 163,840 位（每像素 0.75 位），平均 PSNR 约为 52.38 dB。相对熵的平均值、相对熵的差值、标准差和互相关分别为 7.3242、0.0382、65.0539 和 0.9838，分别为第一个子样本。与现有技术相比，对于 1, 30,000 位的有效载荷，它显示了大约 3 dB 的增加。此外，值得一提的是，所提出的方案具有较低的计算复杂度，因此可用于电子医疗保健应用。鉴于该方案的所有属性以及较低的计算复杂度，它适用于云计算等分布式环境中的 EHI 安全。