ABSTRACT

The development of Internet of Things (IoT) technologies allowed the rapid generation of massive amounts of data by people. Although moving data to a server is a useful solution for storage, the owner of the data loses control, which results in security lapses. An efficient method for cloud-based data models is data integrity. In order to protect data privacy in IoT healthcare, this paper develops a data integrity technique. The Data Owner, IoT server, Key Generator Center, and Auditor are the four entities that make up this model. Here, the ability to verify the accuracy of the outsourced data is present in the auditor, data owner, and IoT server. The setup, storage, and verification phase are the three stages that make up the data integrity model. Here, the proposed Taylor-based Border Collie Cat optimisation is used to generate integrity keys in the best possible way. Here, the Taylor series and Border Collie Cat optimisation (BCCO) are combined to derive the proposed Taylor-based BCCO. Python is used to carry out the proposed strategy’s implementation. The proposed method offered enhanced performance with highest normalised variance of 0.710, highest conditional privacy of 2.880, and smallest computation time of 0.179 sec using heart disease dataset.

摘要

物联网 (IoT) 技术的发展使人们能够快速生成海量数据。尽管将数据移动到服务器是一种有用的存储解决方案，但数据的所有者会失去控制，从而导致安全漏洞。基于云的数据模型的一种有效方法是数据完整性。为了保护物联网医疗保健中的数据隐私，本文开发了一种数据完整性技术。数据所有者、IoT 服务器、密钥生成器中心和审计员是构成此模型的四个实体。在这里，验证外包数据准确性的能力存在于审计员、数据所有者和物联网服务器中。设置、存储和验证阶段是构成数据完整性模型的三个阶段。这里，提议的基于泰勒的博德牧羊犬优化用于以最佳方式生成完整性密钥。在这里，结合了泰勒级数和博德牧羊犬猫优化 (BCCO) 来推导所提出的基于泰勒的 BCCO。Python 用于执行建议策略的实施。所提出的方法提供了增强的性能，最高归一化方差为 0.710，最高条件隐私为 2.880，使用心脏病数据集的最小计算时间为 0.179 秒。