Layers Based Optimal Privacy Preservation of the On-premise Data Supported by the Dual Authentication and Lightweight on Fly Encryption in Cloud Ecosystem

The Big Data stored in the cloud-based clusters of nodes requires an efficient mechanism to protect its privacy information. The traditional anonymization approach for privacy preservation is not applicable for Big Data due to overheads induced as the data storage mechanism follows a distributed file system in the cloud eco-store. This paper presents a dual-layer security model that mitigates the attackers' effect on access to private information. The model architecture consists of a strong authentication mechanism where the key generation to get the access control adopts a high random and customization policy so that at first hand the intruder's probability of entering into the cloud system is nullified and effectively handles the anonymity attack, in the second part of the security model the privacy information part of the data is encrypted with a very lightweight encryption method, and it gets synchronized with the data-deduplication template of the data nodes in the cloud so that the proposed model provides higher security of the privacy information in less time complexities of the cryptographic algorithm which makes the models more reliable as well as flexible to adopt it in the real-time scenario. The behavioral analysis of the proposed Auth-PP for the file-token generation system becomes stable with the incremental file size and exhibits a consistency measure (Ct) = 0.56, which is a mean orient pattern that shows strong stability against the file size so quite adaptable for the big data. The computational performance analysis for cost assessment of encryption and decryption process shows 72% performance improvement for running time for variable file sizes and also exhibits the superior outcome of overall 69.9% for file chunking into the data node on the respective cloud. For the decryption process also, it is observed that the formulated approach attains superior performance in terms of time complexity.