The collection of pervasive data from e-healthcare system inherits potential medical significance through the mode of data exchange with the service providers of professional health care. The sensitive data exchange between the health care providers need to satisfy the requirements of user privacy, since the environment of fog computing is highly vulnerable due to the injection of false data from the hybrid IoT devices. However, sharing health data introduces a diversified number of security issues that include privacy leakage and access control with the further possibility of facing crucial challenges for attaining significant data investigation and services. In this paper, a contextual fully homomorphic encryption techniques-based privacy preserving framework (CFHET-PPF) for securing fog-assisted health data exchanging applications. This proposed CFHET-PPF framework integrates three significant fully homomorphic encryption approaches together in preventing false data injection. It is proposed for facilitating the fog nodes to categorize the shared data based on disease risks for indispensable health data analysis. It aids in achieving a maximum reduction in the number of encryptions by offloading a part of storage and computation burden at the side of the patients to the fog nodes. The security investigations of the proposed CFHET-PPF framework confirmed its superiority in fine grained access control, lightweight process and confidentiality with collusion resistance.

通过与专业医疗保健服务提供商的数据交换模式，从电子医疗保健系统收集的普适数据继承了潜在的医学意义。医护人员之间的敏感数据交换需要满足用户隐私的要求，因为由于从混合物联网设备注入虚假数据，雾计算环境非常脆弱。但是，共享健康数据会引入多种安全问题，其中包括隐私泄漏和访问控制，还可能面临着获得重要数据调查和服务的重大挑战。在本文中，基于上下文的完全同态加密技术的隐私保护框架（CFHET-PPF）用于保护雾辅助健康数据交换应用程序。提出的CFHET-PPF框架将三种重要的完全同态加密方法集成在一起，可防止错误数据注入。为了促进雾节点根据疾病风险对共享数据进行分类，提出了必不可少的健康数据分析方法。通过将患者一侧的存储和计算负担的一部分转移到雾节点，有助于最大程度地减少加密次数。对所提出的CFHET-PPF框架的安全性研究证实了其在细粒度访问控制，轻量级过程和具有抗勾结性的机密性方面的优势。为了促进雾节点根据疾病风险对共享数据进行分类，提出了必不可少的健康数据分析方法。通过将患者一侧的存储和计算负担的一部分转移到雾节点，有助于最大程度地减少加密次数。对所提出的CFHET-PPF框架的安全性研究证实了其在细粒度访问控制，轻量级过程和具有抗勾结性的机密性方面的优势。为了促进雾节点根据疾病风险对共享数据进行分类，提出了必不可少的健康数据分析方法。通过将患者一侧的存储和计算负担的一部分转移到雾节点，有助于最大程度地减少加密次数。对所提出的CFHET-PPF框架的安全性研究证实了其在细粒度访问控制，轻量级过程和具有抗勾结性的机密性方面的优势。