In this paper, an efficient lightweight cloud-based data security model (LCDS) is proposed for building a secured cloud database with the assistance of intelligent rules, data storage, information collection, and security techniques. The major intention of this study is to introduce a new encryption algorithm to secure intellectual data, proposing a new data aggregation algorithm for effective data storage and improved security, developing an intelligent data merging algorithm for accessing encrypted and original datasets. The major benefit of the proposed model is that it is fast in the encryption process at the time of data storage and reduced decryption time during data retrieval. In this work, the authors proposed an enhanced version of the hybrid crypto algorithm (HCA) for cloud data access and storage. The proposed system provides secured storage for storing data within the cloud.

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Cloud services are distributed from manifold sources with autonomous security procedures. The conventional authentication methods result in asynchronous security towards multi-resource access and sharing. This provides volatile authentication for sequential service sessions. For alleviating this issue, a Rendezvous Block-based Authentication Framework (RBAF) is proposed. This framework is backboned with a blockchain paradigm that differentiates authentication based on rendezvous and asynchronous attributes. The modifications in initial and final attributes are observed, intended for training, and supplanted with service-dependent authentications. In the different sessions, attribute-based authentication and agreed end-to-end security are administered using agreed keys that are valid within the sessions. This key generation is modified using the new session and user attributes based on learning recommendations. The ledger paradigm records the session and its associated attributes for different training instances, based on which service flexibility is ensured. The proposed framework's performance is verified using the metrics service distribution ratio, false rate, session uptime, and service delay.