Cloud computing is an eminent technology for providing a data storage facility with efficient storage, maintenance, management and remote backups. Hence, user data are shifted from customary storage to cloud storage. In this transfer, the sensitive attributes are also shifted to cloud storage with high-end security. Current security techniques are processed with high encryption time and provide identical security of entire data with single key dependent. These processes are taking high computational time and leaks entire information if the key is hacked. The proposed Group Key Based Attribute Encryption using Modified Random Fibonacci Cryptographic (MRFC) technique rectifies these issues. Instead of machine learning technique, data owner preference-based attributes segregation is used to divide an input dataset into sensitive and non-sensitive attribute groups. Based on inter-organization usage and data owner’s willingness, sensitive attribute is divided into ‘n + 1′ subgroups and each subgroup is encrypted by ‘n + 1’ group keys. The encrypted sensitive subgroups are merged with non-sensitive attributes and uploaded into a private cloud. The novelties of this paper are, (1) data owner preferred sensitive attribute classification instead of machine learning algorithms, (2) sensitive attribute encryption instead of entire attributes, (3) To reduce encryption time without compromising data owner privacy, (4) To decrypt and access the required subgroup instead of the entire attribute. Our experimental results show that, the proposed method takes minimal processing time, better classification accuracy and minimal memory space with high security to selected attributes as compared to existing classification and security techniques. Hence, sensitive data security and privacy is achieved with minimal processing cost.

云计算是一项杰出的技术，旨在为数据存储设施提供有效的存储，维护，管理和远程备份。因此，用户数据从常规存储转移到云存储。在此转移中，敏感属性也转移到具有高端安全性的云存储中。当前的安全技术以较高的加密时间进行处理，并通过单个密钥提供对整个数据的相同安全性。如果密钥被黑客入侵，这些过程将花费大量的计算时间，并且会泄漏全部信息。拟议的使用改进的随机斐波那契加密（MRFC）技术的基于组密钥的属性加密纠正了这些问题。代替机器学习技术，数据所有者基于首选项的属性隔离用于将输入数据集分为敏感和非敏感属性组。根据组织间的使用情况和数据所有者的意愿，将敏感属性划分为“ n + 1”个子组，并且每个子组都由“ n + 1”个组密钥加密。加密的敏感子组与非敏感属性合并，然后上传到私有云。本文的新颖之处在于：（1）数据所有者首选敏感属性分类，而不是机器学习算法；（2）敏感属性加密，而不是整个属性；（3）减少加密时间而不损害数据所有者的隐私，（4）解密并访问所需的子组，而不是整个属性。我们的实验结果表明，该方法所需的处理时间最短，与现有的分类和安全技术相比，具有更好的分类准确性和最小的存储空间，并且对选定属性具有较高的安全性。因此，以最小的处理成本实现了敏感的数据安全性和保密性。