A group-key-based sensitive attribute protection in cloud storage using modified random Fibonacci cryptography

• Published in: Complex & intelligent systems 2021-08, Vol.7 (4), p.1733-1747
• Main Authors: Sumathi, M., Sangeetha, S.
• Format: Article
• Language: English
• Subjects: Algorithms; Backups; Classification; Cloud computing; Complexity; Computational Intelligence; Computing time; Cryptography; Data integrity; Data storage; Data Structures and Information Theory; Encryption; Engineering; Information storage; Machine learning; Original Article; Privacy; Security; Storage facilities; Subgroups
• ISSN: 2199-4536; 2198-6053
• DOI: 10.1007/s40747-020-00162-3

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.