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Spherical Microaggregation: Anonymizing Sparse Vector Spaces
IIIA, Institut d'Investigació en Intelligència Artificial – CSIC, Consejo Superior de Investigaciones Científicas, Bellaterra, Spain / UAB, Universitat Autónoma de Barcelona, Bellaterra, Spain.
DEIC, Dep. Enginyeria de la Informació i de les Comunicacions, UAB, Universitat Autònoma de Barcelona, Bellaterra, Spain.
Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. IIIA, Institut d'Investigació en Intelligència Artificial – CSIC, Consejo Superior de Investigaciones Científicas, Bellaterra, Spain. (Skövde Artificial Intelligence Lab (SAIL))ORCID-id: 0000-0002-0368-8037
2015 (engelsk)Inngår i: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 49, s. 28-44Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Unstructured texts are a very popular data type and still widely unexplored in the privacy preserving data mining field. We consider the problem of providing public information about a set of confidential documents. To that end we have developed a method to protect a Vector Space Model (VSM), to make it public even if the documents it represents are private. This method is inspired by microaggregation, a popular protection method from statistical disclosure control, and adapted to work with sparse and high dimensional data sets.

sted, utgiver, år, opplag, sider
Elsevier, 2015. Vol. 49, s. 28-44
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Forskningsprogram
Teknik; Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
URN: urn:nbn:se:his:diva-10653DOI: 10.1016/j.cose.2014.11.005ISI: 000350519300003Scopus ID: 2-s2.0-84918506621OAI: oai:DiVA.org:his-10653DiVA, id: diva2:787893
Forskningsfinansiär
EU, FP7, Seventh Framework ProgrammeTilgjengelig fra: 2015-02-12 Laget: 2015-02-12 Sist oppdatert: 2018-03-28bibliografisk kontrollert

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