The Cloud-based storage is able to store more information in gigabyte size in all formats such as text, image or video and it can access at any time with their login credentials. In such a system, reducing the duplication of data and increasing security is an important factor for efficient storage. In this work, the file level de-duplication process is applied on the Magnetic Resonance Imaging (MRI) brain image by reducing the shares of the image to retrieve an original image from the cloud. To reduce the storage problem in this an optimization-based RSSS is used. The objective of this investigation is to decrease the storage blow-up problem in Cloud storage and reduce the duplicate files in the Cloud storage of the health care centre. The proposed model comprises of two subsets: In the first set, the input image is divided into a number of shares using RSSS scheme. In the second set, the minimum share is determined by using the optimization process and it is encrypted and it is stored in the Cloud. Initially, the image is divided into number of shares for reconstructing using the ramp secret sharing scheme.Without these shares, the original image cannot be recovered. But storing all the shares result in high storage capacity. It is overcome with the help of Ant Lion optimization (ALO) to determine the minimum number of shares required for recovering the image. The ALO works to minimizing the Mean Square Error (MSE) of the image reconstruction to find the minimum shares. Then, the minimum shares are encrypted and converted into hash keys. Those hash keys are stored in the Cloud storage. The proposed ALO-RSSS is achieved its objective by reducing the shares to 2 as compared to the traditional method as well as the PSNR is 27% improved.
Due to the development of technology in medicine, millions of healthrelated data such as scanning the images are generated. It is a great challenge to store the data and handle a massive volume of data. Healthcare data is stored in the cloud-fog storage environments. This cloud-Fog based health model allows the users to get health-related data from different sources, and duplicated information is also available in the background. Therefore, it requires an additional storage area, increase in data acquisition time, and insecure data replication in the environment. This paper is proposed to eliminate the de-duplication data using a window size chunking algorithm with a biased sampling-based bloom filter and provide the health data security using the Advanced Signature-Based Encryption (ASE) algorithm in the Fog-Cloud Environment (WCA-BF + ASE). This WCA-BF + ASE eliminates the duplicate copy of the data and minimizes its storage space and maintenance cost. The data is also stored in an efficient and in a highly secured manner. The security level in the cloud storage environment Windows Chunking Algorithm (WSCA) has got 86.5%, two thresholds two divisors (TTTD) 80%, Ordinal in Python (ORD) 84.4%, Boom Filter (BF) 82%, and the proposed work has got better security storage of 97%. And also, after applying the de-duplication process, the proposed method WCA-BF + ASE has required only less storage space for various file sizes of 10 KB for 200, 400 MB has taken only 22 KB, and 600 MB has required 35 KB, 800 MB has consumed only 38 KB, 1000 MB has taken 40 KB of storage spaces.
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