Abstract
Data is undoubtedly one of the most significant assets in the current competitive era and to ensure its value is retained, data safety emerges as a principle concern. Another technology asset is the cloud that is proficient in storing data at very little cost or even no cost at all. There are two main challenges that come with storing data in the cloud, safe storage of data and another is encryption of data with as little time and storage space as possible. These challenges have come in the way of various financial and government organizations tapping the benefits of the cloud. A two-step solution is illustrated in this research study in response to the raised issue. The initial part of this study discusses the classification of data into sensitive data and non-sensitive data. This is done to ensure that precious resources are used only to encrypt sensitive data and are not wasted on encryption data that doesn’t require encryption. To implement this, a mechanism based on Convolutional Neural Network with Logistic Regression(CNN-LR) is proposed. The next phase of this research work discusses the encryption method that is mainly focused on ensuring time and space complexity while encrypting data. For this, Ellipticcurve Diffie Hellman and Shifted Adaption Homomorphism Encryption (ECDH-SAHE) has been used and for decryption of data, Elliptic-curve DiffieHellman-Shifted Adaption Homomorphism Decryption (ECDH-SAHD) has been used. The proposed approach is titled Sensitive Encrypted Storage (SES). The results from the proposed methods are highly motivating and present great efficiency and capability which shall provide new dimension to the researchers in the future.
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Gupta, G., Lakhwani, K. An enhanced approach to improve the encryption of big data using intelligent classification technique. Multimed Tools Appl 81, 25171–25204 (2022). https://doi.org/10.1007/s11042-022-12401-5
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DOI: https://doi.org/10.1007/s11042-022-12401-5