Department of Computer Science and Engineering, GITAM Institute of Technology, GITAM Deemed to be University, Visakhapatnam 530045, India.
Department of Artificial Intelligence & Data Science, Annamacharya Institute of Technology and Sciences, Rajampet 516115, India.
Sensors (Basel). 2021 Nov 2;21(21):7300. doi: 10.3390/s21217300.
As a result of the limited resources available in IoT local devices, the large scale cloud consumer's data that are produced by IoT related machines are contracted out to the cloud. Cloud computing is unreliable, using it can compromise user privacy, and data may be leaked. Because cloud-data and grid infrastructure are both growing exponentially, there is an urgent need to explore computational sources and cloud large-data protection. Numerous cloud service categories are assimilated into numerous fields, such as defense systems and pharmaceutical databases, to compute information space and allocation of resources. Attribute Based Encryption (ABE) is a sophisticated approach which can permit employees to specify a higher level of security for data stored in cloud storage facilities. Numerous obsolete ABE techniques are practical when applied to small data sets to generate cryptograms with restricted computational properties; their properties are used to generate the key, encrypt it, and decrypt it. To address the current concerns, a dynamic non-linear polynomial chaotic quantum hash technique on top of secure block chain model can be used for enhancing cloud data security while maintaining user privacy. In the proposed method, customer attributes are guaranteed by using a dynamic non- polynomial chaotic map function for the key initialization, encryption, and decryption. In the proposed model, both organized and unorganized massive clinical data are considered to be inputs for reliable corroboration and encoding. Compared to existing models, the real-time simulation results demonstrate that the stated standard is more precise than 90% in terms of bit change and more precise than 95% in terms of dynamic key generation, encipherment, and decipherment time.
由于物联网本地设备资源有限，物联网相关机器产生的大规模云消费者数据被外包到云端。云计算不可靠，使用它会损害用户隐私，并且数据可能会被泄露。由于云数据和网格基础设施都在呈指数级增长，因此迫切需要探索计算资源和云大数据保护。许多云服务类别被整合到许多领域，如防御系统和制药数据库，以计算信息空间和资源分配。基于属性的加密（ABE）是一种复杂的方法，可以允许员工为存储在云存储设施中的数据指定更高的安全级别。当应用于小数据集时，许多过时的 ABE 技术在生成具有受限计算属性的密文方面非常实用；它们的属性用于生成密钥、加密和解密。为了解决当前的问题，可以在安全区块链模型之上使用动态非线性多项式混沌量子散列技术来提高云数据安全性，同时保护用户隐私。在提出的方法中，使用动态非多项式混沌映射函数对密钥初始化、加密和解密进行客户属性保证。在提出的模型中，组织和非组织的大量临床数据都被视为可靠验证和编码的输入。与现有模型相比，实时仿真结果表明，该标准在比特变化方面的准确率超过 90%，在动态密钥生成、加密和解密时间方面的准确率超过 95%。
