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IoT Application-enabled Deep Learning Model With Secure ECC-Based Cloud Data Storage Optimization Strategy for Data Deduplication
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Author(s): Manjunath Singh H. (UVCE, India)and R. Tanuja (UVCE, India)
Copyright: 2026
Pages: 28
Source title:
Pioneering AI and Data Technologies for Next-Gen Security, IoT, and Smart Ecosystems
Source Author(s)/Editor(s): Ferdin Joe John Joseph (Thai-Nichi Institute of Technology, Thailand), Karthikeyan Chinnusamy (Veritas, USA), Joseph Jeganathan (University of Bahrain, Bahrain), Ahmed J. Obaid (University of Kufa, Iraq)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3373-4672-4.ch007
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Abstract
Due to the development of the “Internet of Things (IoT),” a huge quantity of data is transferred to the cloud architecture. As a consequence, expenses and storage charges are associated with cloud servers. Storage capacity can be enhanced by implementing a deduplication technique to spot duplicate information. Both cryptography and deduplication are carried out using the full hash values of the information chunks. The deduplication systems are vulnerable to file threats. So, we developed an effective optimal key-based deduplication model. The proposed model uses attributes including filename, size, block name, size, type of file, and data pattern for deduplication. The “Long Short-Term Memory (LSTM)” model is introduced for effective deduplication performance. The LSTM model separates the attributes and deduplicated attributes once the data file is not duplicated. Once it is proved that the data are deduplicated, the edge node (client) enciphers the data by utilizing the Optimal Key-Based Elliptical Curve Cryptography (OK-ECC). Further, it passes the data to the cloud system.
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