A distortion-free watermarking approach for verifying integrity of relational databases

Siledar, Seema; Tamane, Sharvari

doi:10.1109/icsidempc49020.2020.9299653

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Authors in [72] have adapted the MapReduce paradigm for watermarking of relational databases to decrease the computational cost and have implemented distortion-free algorithms in both sequential and MapReduce form. The proposal in [73] generates an image as a watermark from the database content. In [74], each column (attribute) is organized into groups, each having g data elements.…”

Section: E: Othersmentioning

confidence: 99%

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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Section: E: Othersmentioning

confidence: 99%

Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

Rani

Halder

2022

IEEE Access

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“…The behavior of recurrent selection is described by the weight-based capacity metric c w also introduced in [1] according to (5), where the number of times the meta-mark m k is selected, is given by the function κ(m k ). The weight-based capacity constitutes the accumulation of every time each metamark was selected.…”

Section: F(t I [Pk]) = H(sk • H(sk • T I [Pk]))mentioning

confidence: 99%

“…Usually, distortion-free techniques are defined as fragile, while distortion-based as robust approaches, to the extent that the embedded information survives at malicious or accidental attempts to remove it [1], [55]. Relational watermarking techniques can also be classified by their (i) cover-type, defining the type of data of the attribute in R selected to embed the marks; (ii) intent, i.e., ownership protection [1]- [3], [56], data tampering detection [4], [5], [57], traitor tracing [58]- [62], among others; (iii) watermark source, which can be meaningless such as a random binary stream [19], [31], or meaningful, i.e., a source for watermark generation presenting a meaning that does not depend on the watermarking technique [17], [64], [65]. Regarding the covertype, some techniques embed the watermark into attributes storing categorical values [66]- [69], information regarding date or time [70], textual [1], [47] or numerical content [17], [71], [72].…”

Section: Related Workmentioning

confidence: 99%

“…D IGITAL watermarking has become popular for providing evidence of ownership [1]- [3] or data tampering [4], [5], among others, and it allows the benefits of data spreading on the internet while protecting intellectual property. Firstly proposed as a field of information hiding techniques to secure multimedia data, watermarking approaches were also defined for relational data, considering the growth of web-based services with the needs of storing and sharing countless information.…”

Section: Introductionmentioning

confidence: 99%

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Reducing Multiple Occurrences of Meta-Mark Selection in Relational Data Watermarking

2022

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Contrary to multimedia data watermarking approaches, it is not recommended that relational data watermarking techniques consider sequential selection for marks in the watermark and embedding locations in the digital asset being protected. Indeed, considering the database relations' elements, i.e., tuple and attributes, when watermarking techniques are based on sequential processes, watermark detection can be easily compromised by performing subset reverse order attacks. As a result, attackers can obtain owner evidence-free high-quality data since no data modifications for mark removing are required for the malicious operation to succeed. A standard solution to this problem has been pseudo-random selection, which however often leads to choosing the same marks multiple times, ignoring others, thus compromising the embedding of the entire watermark. This work proposes an engine that contributes to control marks' recurrent selection, allowing marks excluded by previous approaches to be considered. The experiments performed show a dramatic improvement of the embedded watermark quality when the proposed engine is included in watermarking techniques' architecture. They also provide evidence that this proposal leads to higher resilience against common malicious operations such as subset and superset attacks.

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A Quantile-Based Watermarking Approach for Distortion Minimization

Gort

Olliaro

Cortesi

2022

Foundations and Practice of Security

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Distortion-based watermarking techniques embed the watermark by performing tolerable changes in the digital assets being protected. For relational data, mark insertion can be performed over the different data types of the database relations’ attributes. An important goal for distortion-based approaches is to minimize as much as possible the changes that the watermark embedding provokes into data, preserving their usability, watermark robustness, and capacity. This paper proposes a quantile-based watermarking technique for numerical cover type focused on preserving the distribution of attributes used as mark carriers. The experiments performed to validate our proposal show a significant distortion reduction compared to traditional approaches while maintaining watermark capacity levels. Also, positive achievements regarding robustness are visible, evidencing our technique’s resilience against subset attacks.

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A distortion-free watermarking approach for verifying integrity of relational databases

Cited by 6 publications

References 9 publications

Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

Reducing Multiple Occurrences of Meta-Mark Selection in Relational Data Watermarking

A Quantile-Based Watermarking Approach for Distortion Minimization

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