HDT crypt : Compression and encryption of RDF datasets

García, Javier David Fernández; Kirrane, Sabrina; Polleres, Axel; Steyskal, Simon

doi:10.3233/sw-180335

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…Different security problems have been investigated in the context of graph-structured knowledge, such as the compression and encryption of RDF datasets (Fernández et al 2020) and search over encrypted graph data (Poh, Mohamad, and Z'aba 2012). Methods using some principled ways to introduce noise into knowledge graphs, such as typed sampling, relational sampling, and corrupting positive instances, have been proposed in the literature-e.g., see (Kotnis and Nastase 2017).…”

Section: Related Workmentioning

confidence: 99%

Randomized Generation of Adversary-aware Fake Knowledge Graphs to Combat Intellectual Property Theft

Kang

Molinaro

Pugliese

et al. 2021

AAAI

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Knowledge Graphs (KGs) can be used to store information about software design, biomedical designs, and financial information---all domains where intellectual property and/or specialized knowledge must be kept confidential. Moreover, KGs can also be used to represent the content of technical documents. In order to deter theft of intellectual property via cyber-attacks, we consider the following problem: given a KG K0 (e.g., representing a software or biomedical device design or the content of a technical document), can we automatically generate a set of KGs that are similar enough to K0 (so they are hard to discern as synthetic) but sufficiently different (so as to be wrong)? If this is possible, then we will be one step closer to automatically generating fake KGs that an adversary has difficulty distinguishing from the original. We will also be closer to automatically generating documents corresponding to fake KGs so that an adversary who steals such documents has difficulty distinguishing the real from the fakes. We formally define this problem and prove that it is NP-hard. We show that obvious approaches to solving this problem do not satisfy a novel concept of "adversary-awareness" that we define. We provide a graph-theoretic characterization of the problem and leverage it to devise an "adversary-aware" algorithm. We validate the efficacy of our algorithm on 3 diverse real-world datasets, showing that it achieves high levels of deception.

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Section: Related Workmentioning

confidence: 99%