Copyright protection method of big data based on nash equilibrium and constraint optimization

Xu, Yabin; Shi, Bin

doi:10.1007/s12083-021-01096-4

Cited by 4 publications

(1 citation statement)

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“…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]. Note that the relational watermarking approach we propose in this work is distortion-based, oriented to protect the ownership of the data to which it is applied.…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%

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

2022

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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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