A Fingerprinting Technique for Numeric Relational Databases with Distortion Minimization

Mohanpurkar, Arti; Joshi, Madhuri S.

doi:10.1109/iccubea.2015.134

Cited by 5 publications

(6 citation statements)

References 13 publications

(15 reference statements)

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“…As a result, the specific buyer whose data were leaked can be identified. Some recent works presented in [6][7][8] use different bits for identifying the different users of the data. However, these techniques do not provide any mechanism to associate the mark bits used for fingerprinting to the relevant user to a biometric feature of the user.…”

Section: Scenariomentioning

confidence: 99%

“…Our claim is further strengthen by the fact that Mallory cannot afford to launch attacks that would affect the data to such an extent that its usability is compromised. Mathematically, P Attack (success) = (0.5) γ (7) This probability of successful malicious attacks is quite high, but the embedding of each bit of multi-bit fingerprint helps in making the fingerprint robust by decoding it from other tuples in case it has been compromised from some tuples after the attack. Moreover, as is evident from Equation ( 7), selection of higher percentage of tuples for fingerprint embedding can also help to improve fingerprint robustness, but in this case more data distortions will be introduced.…”

Section: Some Marked Tuples Can Have Different Values In Both the Dat...mentioning

confidence: 99%

“…This section presents the comparison of proposed technique with the techniques of Guo et al [21] and Ahmad et al [6] for resilience against different types of attacks. It is important to mention here that we chose this technique of year 2006 because, to the best of our knowledge, there is no significant work done for the fingerprinting of numeric data after this technique; although, some recent works have been proposed in [7,33,34] but they are not prestigious. There is another prestigious technique proposed in [35] but it considers that all the purchasers (or data recipients) share the same constraints which is not practical as different applications of the same data have different constraints.…”

Section: Comparison Of Proposed Technique With a State-of-art Techniq...mentioning

confidence: 99%

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Fingerprinting of Relational Databases for Stopping the Data Theft

et al. 2020

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The currently-emerging technology demands sharing of data using various channels via the Internet, disks, etc. Some recipients of this data can also become traitors by leaking the important data. As a result, the data breaches due to data leakage are also increasing. These breaches include unauthorized distribution, duplication, and sale. The identification of a guilty agent responsible for such breaches is important for: (i) punishing the culprit; and (ii) preventing the innocent user from accusation and punishment. Fingerprinting techniques provide a mechanism for classifying the guilty agent from multiple recipients and also help to prevent the innocent user from being accused of the data breach. To those ends, in this paper, a novel fingerprinting framework has been proposed using a biometric feature as a digital mark (signature). The use of machine learning has also been introduced to make this framework intelligent, particularly for preserving the data usability. An attack channel has also been used to evaluate the robustness of the proposed scheme. The experimental study was also conducted to demonstrate that the proposed technique is robust against several malicious attacks, such as subset selection attacks, mix and match attacks, collusion attacks, deletion attacks, insertion attacks, and alteration attacks.

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Section: Scenariomentioning

confidence: 99%

Section: Some Marked Tuples Can Have Different Values In Both the Dat...mentioning

confidence: 99%

Section: Comparison Of Proposed Technique With a State-of-art Techniq...mentioning

confidence: 99%

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Fingerprinting of Relational Databases for Stopping the Data Theft

et al. 2020

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“…The literature suggests several techniques, which watermark or fingerprint numeric relational databases but have not handled collusion attack avoidance [14,15,16].…”

Section: Effect On Mean and Variance Should Be Minuscule µ = µ And =mentioning

confidence: 99%

“…A technique having features like robustness, minimum distortion for watermarking relational databases is thus proposed. Here the preservation of information is discussed in terms of mean and variance and performs only watermarking.Another approach to achieve collusion avoidance with minimum distortion is discussed in [15,16] and the system is found to be more robust as it takes into account the primary key. This approach is applied on Numeric Relational databases.…”

Section: Related Workmentioning

confidence: 99%

Fingerprinting Numeric Databases with Information Preservation and Collusion Avoidance

Mohanpurkar¹,

Joshi²

2015

IJCA

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Information age demands omnipresence of data. Large data sets are created, maintained and outsourced to the third party experts for data mining. Knowledge and patterns are extracted by using advanced data mining algorithms that assist the decision makers to ensure quick, correct and effective decisions to be made in this world of competition. The outsourcing of these large data sets faces the problem of theft and loss of ownership. The problem of data theft can be handled by fingerprinting i.e. embedding buyer specific marks along with ownership identification marks which further leads to the challenge of knowledge preservation. Thus, a technique which performs fingerprinting with knowledge preservation on numeric relational data to be outsourced is proposed here. It is ensured that the usability constraints are not violated. Knowledge preservation is achieved by optimizing the error to be inserted using Particle Swarm Optimization (PSO), a natureinspired optimization algorithm. Collusion attack is very well-known in the context of fingerprinting techniques. Here, the proposed system provides a mechanism for avoiding collusion. The proposed system is independent of the primary key.

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HQR-Scheme: A High Quality and resilient virtual primary key generation approach for watermarking relational data

Gort

Feregrino-Uribe

Cortesi

et al. 2019

Expert Systems with Applications

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This work propose metrics to allow a precise measuring of the quality of the Virtual Primary Keys (VPK) generated by any VPK scheme proposed so far, without requiring to perform the watermark embedding, so wasting time can be avoided in case of low-quality detection. We also analyze the main aspects to design the ideal VPK scheme, seeking the generation of high-quality VPK sets adding robustness to the process. Finally, a new scheme is presented along with the experiments carried out to validate and compare the results with the rest of the schemes proposed in the literature. We believe that these findings will be of interest to the readers of Elsevier Expert Systems with Applications Journal. This manuscript is original, has not been published before and is not currently being considered for publication elsewhere.

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A Fingerprinting Technique for Numeric Relational Databases with Distortion Minimization

Cited by 5 publications

References 13 publications

Fingerprinting of Relational Databases for Stopping the Data Theft

Fingerprinting of Relational Databases for Stopping the Data Theft

Fingerprinting Numeric Databases with Information Preservation and Collusion Avoidance

HQR-Scheme: A High Quality and resilient virtual primary key generation approach for watermarking relational data

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