Watermarking for Ontologies

Suchanek, Fabian M.; Gross-Amblard, David; Abiteboul, Serge

doi:10.1007/978-3-642-25073-6_44

Cited by 13 publications

(12 citation statements)

References 13 publications

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“…This process is repeated until n facts have been added. 5 We assume a canonical ordering, e.g., alphabetical. Detection.…”

Section: Additive Watermarkingmentioning

confidence: 99%

“…Ontologies on the Semantic Web are usually available under some kind of license, such as the Creative-Commons License (CC) 1 or the GNU General Public License (GPL) 2 . Table 1 lists some of the major ontologies with their licenses [5]. Most licenses give users a free hand to use the data as they please.…”

Section: Introductionmentioning

confidence: 99%

“…If these modifications appear in the suspect ontology, they act as a proof of ownership. Our own work [5] has introduced the concept of subtractive watermarking, where the ontology is marked by removing certain statements. All of these approaches have a considerable drawback: To prove that a suspect ontology "stole data" from the original ontology, the suspect ontology has to be downloaded and checked for the marks.…”

Section: Introductionmentioning

confidence: 99%

“…Previous work (e.g., [1,5]) has focused on sophisticated approaches for ownership proofs, notably on watermarking. Watermarking introduces small modifications into the original ontology.…”

Section: Introductionmentioning

confidence: 99%

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Adding fake facts to ontologies

Suchanek

Gross-Amblard

2012

Proceedings of the 21st International Conference on World Wide Web

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In this paper, we study how artificial facts can be added to an RDFS ontology. Artificial facts are an easy way of proving the ownership of an ontology: If another ontology contains the artificial fact, it has probably been taken from the original ontology. We show how the ownership of an ontology can be established with provably tight probability bounds, even if only parts of the ontology are being re-used. We explain how artificial facts can be generated in an inconspicuous and minimally disruptive way. Our demo allows users to generate artificial facts and to guess which facts were generated.

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“…This process is repeated until n facts have been added. 5 We assume a canonical ordering, e.g., alphabetical. Detection.…”

Section: Additive Watermarkingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Previous work (e.g., [1,5]) has focused on sophisticated approaches for ownership proofs, notably on watermarking. Watermarking introduces small modifications into the original ontology.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adding fake facts to ontologies

Suchanek

Gross-Amblard

2012

Proceedings of the 21st International Conference on World Wide Web

Self Cite

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show abstract

“…Due to the list representation, watermarks are encoded in the execution state of the application rather than in its syntax, which makes it robust against attacks. Suchanek et al propose an interesting approach for watermarking ontologies [46]. In this approach the authors propose to rather remove existing information than adding new information or modifying existing information.…”

Section: Watermarkingmentioning

confidence: 99%

Data Lineage in Malicious Environments

Backes¹,

Grimm²,

Kate³

2016

IEEE Trans. Dependable and Secure Comput.

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Intentional or unintentional leakage of confidential data is undoubtedly one of the most severe security threats that organizations face in the digital era. The threat now extends to our personal lives: a plethora of personal information is available to social networks and smartphone providers and is indirectly transferred to untrustworthy third party and fourth party applications. In this work, we present a generic data lineage framework LIME for data flow across multiple entities that take two characteristic, principal roles (i.e., owner and consumer). We define the exact security guarantees required by such a data lineage mechanism toward identification of a guilty entity, and identify the simplifying non-repudiation and honesty assumptions. We then develop and analyze a novel accountable data transfer protocol between two entities within a malicious environment by building upon oblivious transfer, robust watermarking, and signature primitives. Finally, we perform an experimental evaluation to demonstrate the practicality of our protocol and apply our framework to the important data leakage scenarios of data outsourcing and social networks. In general, we consider LIME , our lineage framework for data transfer, to be an key step towards achieving accountability by design. ✦ 1545-5971 (c)

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Completing Predicates Based on Alignment Rules from Knowledge Graphs

Niazmand,

Vidal

2024

Lecture Notes in Computer Science

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Watermarking for Ontologies

Cited by 13 publications

References 13 publications

Adding fake facts to ontologies

Adding fake facts to ontologies

Data Lineage in Malicious Environments

Completing Predicates Based on Alignment Rules from Knowledge Graphs

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