论文标题
提议的基于访问控制的隐私保护模型,用于在云中共享医疗保健数据
A Proposed Access Control-Based Privacy Preservation Model to Share Healthcare Data in Cloud
论文作者
论文摘要
云计算中的医疗保健数据通过在医疗保健提供者之间共享有关医疗咨询的个人健康数据的信息来促进患者的治疗。此外,保留数据和患者身份的机密性是另一个具有挑战性的任务。本文介绍了基于访问控制的概念(AC)隐私保护模型,用于对拟议的数字系统中用户和数据所有者的相互认证。提出的模型提供了高安全性保证和高效率。提出的数字系统由四个不同的实体,用户,数据所有者,云服务器和密钥生成中心(KGC)组成。这种方法使系统更加稳健和高度安全,已通过多种情况进行了验证。此外,提出的模型包括设置阶段,密钥生成阶段,加密阶段,验证阶段,访问控制阶段和数据共享阶段。设置阶段由数据所有者运行,该数据所有者将输入作为安全参数并生成系统主密钥和安全参数。然后,在密钥生成阶段,私钥由kgc生成,并存储在云服务器中。之后,生成的私钥被加密。然后,会话密钥由KGC生成,并授予用户和云服务器存储,然后使用验证消息在验证阶段进行验证。最后,与用户共享数据并在用户端解密。所提出的模型优于其他方法,最大真实数据速率为0.91。
Healthcare data in cloud computing facilitates the treatment of patients efficiently by sharing information about personal health data between the healthcare providers for medical consultation. Furthermore, retaining the confidentiality of data and patients' identity is a another challenging task. This paper presents the concept of an access control-based (AC) privacy preservation model for the mutual authentication of users and data owners in the proposed digital system. The proposed model offers a high-security guarantee and high efficiency. The proposed digital system consists of four different entities, user, data owner, cloud server, and key generation center (KGC). This approach makes the system more robust and highly secure, which has been verified with multiple scenarios. Besides, the proposed model consisted of the setup phase, key generation phase, encryption phase, validation phase, access control phase, and data sharing phase. The setup phases are run by the data owner, which takes input as a security parameter and generates the system master key and security parameter. Then, in the key generation phase, the private key is generated by KGC and is stored in the cloud server. After that, the generated private key is encrypted. Then, the session key is generated by KGC and granted to the user and cloud server for storing, and then, the results are verified in the validation phase using validation messages. Finally, the data is shared with the user and decrypted at the user-end. The proposed model outperforms other methods with a maximal genuine data rate of 0.91.