Open Access

ARTICLE

Health Data Deduplication Using Window Chunking-Signature Encryption in Cloud

G. Neelamegam^*, P. Marikkannu

Department of Computer Science and Engineering, Anna University Regional Campus Coimbatore, Coimbatore, 641046, India

* Corresponding Author: G. Neelamegam. Email:

Intelligent Automation & Soft Computing 2023, 36(1), 1079-1093. https://doi.org/10.32604/iasc.2023.031283

Received 14 April 2022; Accepted 25 May 2022; Issue published 29 September 2022

Abstract

Due to the development of technology in medicine, millions of health-related 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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Keywords

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