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dc.contributor.authorİşler, Devriş 
dc.contributor.authorCabana, Elisa 
dc.contributor.authorLaoutaris, Nikolaos 
dc.date.accessioned2022-10-11T09:53:37Z
dc.date.available2022-10-11T09:53:37Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/20.500.12761/1626
dc.description.abstractWe present a novel technique for modulating the appearance frequency of a few tokens within a dataset for encoding an invisible watermark that can be used to protect ownership rights upon data. We develop optimal as well as fast heuristic algorithms for creating and verifying such watermarks. We also demonstrate the robustness of our technique against various attacks and derive analytical bounds for the false positive probability of erroneously “detecting” a watermark on a dataset that does not carry it. Our technique is applicable to both single dimensional and multidimensional datasets, is independent of token type, and can be used in a variety of use cases that involve buying and selling data in contemporary data marketplaces.es
dc.language.isoenges
dc.publisherIMDEA Networks Technical Reportes
dc.titleFreqyWM: Frequency WaterMarking for the New Data Economyes
dc.typetechnical reportes
dc.rights.accessRightsopen accesses
dc.monograph.typetechnical_reportes
dc.page.total15es
dc.identifier.reportTR-Institute IMDEA-Networks-2022-1es


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