Banking-Based Sensitivity Level Analysis Framework for Secure and Authenticated Cloud Data Using Multi-Technique Cryptographic Integration

The protection of data and applications in the cloud is ensured by Cloud Security (CS), whereas bank details are safeguarded from unauthorized access by authentication. However, the existing techniques failed to focus on the data’s sensitivity level, thus leading to inadequate protection of significant information in the cloud. The novelty of this work lies in the design of a sensitivity-aware, multi-technique cryptographic framework that integrates [Formula: see text]S-based Fuzzy ([Formula: see text]SFuzzy), Stratified-Convenience-centric Biometric Curve Cryptography (SCBCC) and Vigenère–Caesar Cipher-centric Message Authentication Code (VC 2 -MAC) within a secured Smart Contract and IPFS environment to ensure layered data security and precise authentication. The Data Owner (DO) first registers and logs in to upload bank details to the cloud. The texts are further preprocessed. Then, keywords are extracted from the bank details for categorizing the sensitivity levels of data by utilizing [Formula: see text]SFuzzy. Thereafter, the secured Smart Contract (SC) is created for confidential and restricted data, followed by hashcode generation and Message Authentication Code (MAC) creation. In the meantime, the data is secured and stored in the Interplanetary File System (IPFS). The Cloud User (CU) logs in and sends requests to DO to download the bank details. Finally, the MAC is verified for highly sensitive data within 1099[Formula: see text]ms to decline or download the bank details for authorized users. Thus, this work performed superior to the conventional methods. Moreover, the proposed TDGPS-KAnonymity approach achieved a higher PPR of 98.93%, which is 3.48% higher than the existing K-Anonymity technique. The study signified the proposed approach’s superior effectiveness in preserving data privacy while maintaining performance efficiency.