Reversible Database Watermarking Based on Random Forest and Genetic Algorithm

Ge, Chonghui; Sun, Jian; Sun, Yuxin; Di, Yunlong; Zhu, Yongjin; Xie, Linfeng; Zhang, Yingzhou

doi:10.1109/cyberc49757.2020.00045

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…(Li, Wang, & Luo, 2020) proposed a reversible database watermarking in which a watermark is embedded by histogram non-redundant shifting method. (Ge, et al, 2020) suggested the use of a random forest algorithm to select important attributes for watermark embedding along with genetic algorithm. (Unnikrishnan & Pramod, 2021) proposed a system that uses a set of watermark bits to make a validation and recovery mechanism for database authentication.…”

Section: Introductionmentioning

confidence: 99%

Reversible Database Watermarking With Distortion Control

Siledar¹,

Tamane²

2021

INDJCSE

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Database watermarking has emerged as an effective solution to protect ownership of relational databases. Unlike images and videos, watermark insertion in databases can cause distortion leading to loss of data quality. In this paper, we propose a reversible database watermarking technique with distortion control. Firstly, we need to select optimal attributes for applying difference expansion. Pearson Correlation Coefficient (PCC) is employed to find a pair of attributes with the highest correlation. This ensures controlled distortion in the database. Later, difference expansion is applied on the selected attributes. It makes the method reversible and original data can be recovered whenever required. Experimental results show that the proposed method is robust against insertion, deletion, and modification attacks.

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Section: Introductionmentioning

confidence: 99%

Reversible Database Watermarking With Distortion Control

Siledar¹,

Tamane²

2021

INDJCSE

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Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

Rani

Halder

2022

IEEE Access

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Digital watermarking is considered one of the most promising techniques to verify the authenticity and integrity of digital data. It is used for a wide range of applications, e.g., copyright protection, tamper detection, traitor tracing, maintaining the integrity of data, etc. In the past two decades, a wide range of algorithms for relational database watermarking has been proposed. Even though a number of surveys exist in the literature, they are unable to provide insightful guidance to choose the right watermarking technique for a given application. In this paper, we provide an exhaustive empirical study and thorough comparative analysis of various relational database watermarking techniques in the literature. Our work is different from the existing survey papers as we consider both distortion-based and distortion-free techniques along with a rigorous experimental analysis demonstrating a detailed comparison on robustness, data usability, and computational cost with considerable empirical evidence. INDEX TERMSDigital watermarking, relational database, empirical study, robustness, data usability. RAJU HALDER (Member, IEEE) received the Ph.D. degree from Università Ca´Foscari, Italy, in 2012. He is currently an Associate Professor with the Department of Computer Science and Engineering, IIT Patna. Before joining IIT Patna, he worked as a Postdoctoral Researcher with Macquarie University, Australia. He worked with the Robotics Team at HASLab (University of Minho), Portugal, in 2016. Prior to his Ph.D. degree, he had also worked as an Associate System Engineer at IBM India Private Ltd., from 2007 to 2008. His research interests include formal methods, blockchain technology, program analysis and verification, and data privacy and security.

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A statistical characteristics preserving watermarking scheme for time series databases

Yu,

Ma,

Zhang

et al. 2024

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Database watermarking is one of the most effective methods to protect the copyright of databases. However, traditional database watermarking has a potential drawback: watermark embedding will change the distribution of data, which may affect the use and analysis of databases. Considering that most analyses are based on the statistical characteristics of the target database, keeping the consistency of the statistical characteristics is the key to ensuring analyzability. Since statistical characteristics analysis is performed in groups, compared with traditional relational databases, time series databases (TSDBs) have obvious time-grouping characteristics and are more valuable for analysis. Therefore, this paper proposes a robust watermarking algorithm for time series databases, effectively ensuring the consistency of statistical characteristics. Based on the time-group characteristics of TSDBs, we propose a three-step watermarking method, which is based on linear regression, error compensation, and watermark verification, named RCV. According to the properties of the linear regression model and error compensation, the proposed watermark method generates a series of data that have the same statistical characteristics. Then, the verification mechanism is performed to validate the generated data until it conveys the target watermark message. Compared with the existing methods, our method achieves superior robustness and preserves constant statistical properties better.r.

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Reversible Database Watermarking Based on Random Forest and Genetic Algorithm

Cited by 3 publications

References 14 publications

Reversible Database Watermarking With Distortion Control

Reversible Database Watermarking With Distortion Control

Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

A statistical characteristics preserving watermarking scheme for time series databases

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