Enhanced-Longest Common Subsequence based novel steganography approach for cloud storage.

Autor: Mandal, Sudakshina, Khan, Danish Ali
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Zdroj: Multimedia Tools & Applications; Feb2023, Vol. 82 Issue 5, p7779-7801, 23p
Abstrakt: Cloud storage is an essential and most demanding application for enterprise and private use in today's communication. However, the data security over the distributed cloud resources is threatened due to numerous security challenges. Analyzing and reviewing the existing security policies are necessary to prevent unauthorized infringement of sensitive information in cloud storage. Therefore, a convenient and adaptable security framework should be designed. However, traditional steganographic systems use the embedding technique substantially by transforming carriers which unavoidably leaves the evidence. Therefore the transforming bits can be easily detected through steganalysis. This paper proposes a novel steganography approach to ensure the security of sensitive information in cloud storage. The approach uses the realization over embedding technique, which stores only the cover-secret mapping instead of original information in the cloud by taking significantly less storage space. Enhanced Longest Common Subsequence (Enhanced- LCS) is used here to generate the secure reversible mapping, and data hiding methodology is used to mask the secret in this framework. This mapping is fully realized only by the dedicated recipient with keys. The Cantor pairing function is used for constructing keys. We significantly reduce the time and space complexity by using the Enhanced-LCS algorithm. Space and time complexities are calculated as O(n) and O (n ∗ log (n)) here. Divergent file types such as image, word, and PDF files with different dimensions have been used to perform experimental analysis. This approach also ensures protection against some of the well-known security attacks. Different types of performance analysis make this framework more efficient and robust. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index
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