Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

Rani, Sapana; Halder, Raju

doi:10.1109/access.2022.3157866

Cited by 14 publications

(11 citation statements)

References 97 publications

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“…refs. [45,46]) and the references therein. In contrast to existing schemes, the proposed scheme embeds data into shares rather than attribute values or constrained data contents.…”

Section: Resultsmentioning

confidence: 99%

A novel secure data outsourcing scheme based on data hiding and secret sharing for relational databases

2023

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Data encryption‐based and secret sharing‐based data outsourcing schemes protect the confidentiality of sensitive attributes but not their secrecy. Ciphertexts/shares generated by a data encryption/secret sharing scheme can attract the attention of interceptors. Thus, it is desired to hide the existence of highly‐sensitive attributes (as secret attributes) in the outsourced relations in addition to protecting their contents. This paper proposes a novel scheme that integrates data hiding with secret sharing for relational databases to protect both the secrecy and confidentiality of secret attributes. It embeds one or multiple secret attributes in a relation into one or multiple cover attributes in the same relation. A set of share (and possibly index) columns are constructed such that they are pretended to be associated with only the cover attributes, while those share columns and some virtual share columns can be used to recover both the secret and cover attributes. What interceptors observe in each relation include the attributes stored in plaintext and the share (and possibly index) columns associated with the cover attributes but not any extra column. Thus, they find nothing suspicious. This is the first effective data hiding scheme for relational databases that protects the secrecy of secret attributes.

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“…refs. [45,46]) and the references therein. In contrast to existing schemes, the proposed scheme embeds data into shares rather than attribute values or constrained data contents.…”

Section: Resultsmentioning

confidence: 99%

A novel secure data outsourcing scheme based on data hiding and secret sharing for relational databases

2023

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“…Since the publication of the AHK algorithm, relational data watermarking techniques have increased in number and diversity. For example, some techniques present database protection as a middleware that generates format-independent watermarks [11], some focus on reducing the distortion caused by the watermark embedding [22], and others allow data owners to define constraints to preserve data usability automatically [23]. Despite the many techniques, most works target numerical attributes for mark embedding while modifying their values and trying to control the distortion using statistical metrics as a reference.…”

Section: Related Workmentioning

confidence: 99%

“…Researchers first proposed watermarking techniques to secure multimedia data. Nevertheless, over time, they have been successfully used to protect other digital assets, such as compiled code [10], relational databases [11], neural networks [12], etc. Regarding relational data, considering third parties can readily access them through the Internet, watermarks secure them without restricting access or portability.…”

Section: Introductionmentioning

confidence: 99%

Study of the Watermark Source’s Topology Role on Relational Data Watermarking Robustness

Gort,

Olliaro,

Cortesi

2024

IEEE Access

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Accessing relational databases through web services increases risks of piracy and tampering. Watermarking guarantees data protection without constraining their access and management. Researchers traditionally compare the extracted watermark with the embedded one after simulating attacks to analyze its robustness. However, the literature has not addressed how much watermark signal is retained after querying multi-word textual attributes and how topological features of the watermark affect robustness. In this work, we fill this gap by evaluating a semantics-preserving approach. We execute different classes of queries in three data sets, including an actual database of a public administration. The results show that 99.99% of the marks can be detected when considering a watermark source with a topological factor close to or equal to 1. As valuable contributions, we formally defined the watermark source's topological factor and the corrosion degree of the watermark detection. This work offers hints to data owners to adjust detection parameters, increasing the chances of spotting the watermark despite having, as evidence, data highly damaged due to malicious operations.INDEX TERMS Ownership proof, plagiarism detection, relational data, robust watermarking, watermark topology.

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“…Watermarking techniques for relational databases can be classified in several ways. For instance, by the type of watermark information to embed, the type of attributes into which the marks are embedded, or if the watermark distorts the data or not [9]. In particular, techniques modifying the relation content for its protection are defined as distortion-based, whereas the others as distortionfree.…”

Section: A Architecture Of Relational Database Watermarkingmentioning

confidence: 99%

“…The SSIM is obtained according to (9) and returns an appreciation of the extracted watermark quality closer to human perception. For this case, multiple windows of size N × N are defined by x and y.…”

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

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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Comparative Analysis of Relational Database Watermarking Techniques: An Empirical Study

Cited by 14 publications

References 97 publications

A novel secure data outsourcing scheme based on data hiding and secret sharing for relational databases

A novel secure data outsourcing scheme based on data hiding and secret sharing for relational databases

Study of the Watermark Source’s Topology Role on Relational Data Watermarking Robustness

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

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