Low distortion reversible database watermarking based on hybrid intelligent algorithm
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Published:2023
Issue:12
Volume:20
Page:21315-21336
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ISSN:1551-0018
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Container-title:Mathematical Biosciences and Engineering
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language:
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Short-container-title:MBE
Author:
Cai Chuanda1, Peng Changgen12, Niu Jin1, Tan Weijie123, Tang Hanlin4
Affiliation:
1. State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China 2. College of Computer Science and Technology, Guizhou University, Guiyang 550025, China 3. Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China 4. Guizhou ShuJuBao Network Technology Co.Ltd, Guiyang 550025, China
Abstract
<abstract><p>In many fields, such as medicine and the computer industry, databases are vital in the process of information sharing. However, databases face the risk of being stolen or misused, leading to security threats such as copyright disputes and privacy breaches. Reversible watermarking techniques ensure the ownership of shared relational databases, protect the rights of data owners and enable the recovery of original data. However, most of the methods modify the original data to a large extent and cannot achieve a good balance between protection against malicious attacks and data recovery. In this paper, we proposed a robust and reversible database watermarking technique using a hash function to group digital relational databases, setting the data distortion and watermarking capacity of the band weight function, adjusting the weight of the function to determine the watermarking capacity and the level of data distortion, using firefly algorithms (FA) and simulated annealing algorithms (SA) to improve the efficiency of the search for the location of the watermark embedded and, finally, using the differential expansion of the way to embed the watermark. The experimental results prove that the method maintains the data quality and has good robustness against malicious attacks.</p></abstract>
Publisher
American Institute of Mathematical Sciences (AIMS)
Subject
Applied Mathematics,Computational Mathematics,General Agricultural and Biological Sciences,Modeling and Simulation,General Medicine
Reference33 articles.
1. X. Tang, Z. Cao, X. Dong, J. Shen, PKMark: a robust zero-distortion blind reversible scheme for watermarking relational databases, in 2021 IEEE 15th International Conference on Big Data Science and Engineering (BigDataSE), (2021), 72–79. https://doi.org/10.1109/BigDataSE53435.2021.00020 2. M. L. P. Gort, M. Olliaro, A. Cortesi, C. F. Uribe, Semantic-driven watermarking of relational textual databases, Expert Syst. Appl., 167 (2021), 114013. https://doi.org/10.1016/j.eswa.2020.114013 3. A. S. Alghamdi, S. Naz, A. Saeed, E. Al Solami, M. Kamran, M. S. Alkatheiri, , A novel database watermarking technique using blockchain as trusted third party, Comput. Mater. Con., 70 (2022), 1585–1601. https://doi.org/10.32604/cmc.2022.019936 4. K. E. Drandaly, W. Khedr, A. M. Mostafa, I. Mohamed, A digital watermarking for relational database: state of Art techniques, Int. J. Adv. Sci. Tech., 29 (2020), 870–883. 5. R. Agrawal, P. J. Haas, J. Kiernan, Watermarking relational data: framework, algorithms and analysis, VLDB J., 12 (2003), 157–169. https://doi.org/10.1007/s00778-003-0097-x
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