Ownership protection of shapes with geodesic distance preservation (Contributo in atti di convegno)

Type
Label
  • Ownership protection of shapes with geodesic distance preservation (Contributo in atti di convegno) (literal)
Anno
  • 2008-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1145/1353343.1353379 (literal)
Alternative label
  • Michail Vlachos +; Claudio Lucchese ?; Deepak Rajan +; Philip S. Yu ? (2008)
    Ownership protection of shapes with geodesic distance preservation
    in 11th International Conference on Extending Database Technology - EDBT'08, Nantes, France, 25-30 March 2008
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Michail Vlachos +; Claudio Lucchese ?; Deepak Rajan +; Philip S. Yu ? (literal)
Pagina inizio
  • 276 (literal)
Pagina fine
  • 286 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://dl.acm.org/citation.cfm?doid=1353343.1353379 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#note
  • In: 11th International Conference on Extending Database Technology (Nantes, France, 25-29 March 2008). Proceedings, Electronic Conference Proceedings, 2008. (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#descrizioneSinteticaDelProdotto
  • ABSTRACT: We present rights protection techniques for shape datasets. The embedded ownership seal induces an imperceptible visual distortion and is particularly robust to a variety of transformations, including rotation, translation, scaling, noise addition and resampling. The proposed ownership protection scheme additionally preserves the geodesic distances between the dataset objects, hence preserving the mining capacity of the dataset. We also demonstrate extensions for dendrogram preservation. Our findings are illustrated on image shapes extracted from anthropological and natural science data. (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • + IBM T.J. Watson Research Center ? University of Illinois, Chicago ? University of Venice (literal)
Titolo
  • Ownership protection of shapes with geodesic distance preservation (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-59593-926-5 (literal)
Abstract
  • Protection of one's intellectual property is a topic with important technological and legal facets. The significance of this issue is amplified nowadays due to the ease of data dissemination through the internet. Here, we provide technological mechanisms for establishing the ownership of a dataset consisting of multiple objects. The objects that we consider in this work are shapes (i.e., two dimensional contours), which abound in disciplines such as medicine, biology, anthropology and natural sciences. The protection of the dataset is achieved through means of embedding of an imperceptible ownership 'seal', that imparts only minute visual distortions. This seal needs to be embedded in the proper data space so that its removal or destruction is particularly difficult. Our technique is robust to many common transformations, such as data rotation, translation, scaling, noise addition and resampling. In addition to that, the proposed scheme also guarantees that important distances between the dataset shapes/objects are not distorted. We achieve this by preserving the geodesic distances between the dataset objects. Geodesic distances capture a significant part of the dataset structure, and their usefulness is recognized in many machine learning, visualization and clustering algorithms. Therefore, if a practitioner uses the protected dataset as input to a variety of mining, machine learning, or database operations, the output will be the same as on the original dataset. We illustrate and validate the applicability of our methods on image shapes extracted from anthropological and natural science data. (literal)
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