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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.
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. 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 (English)In: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 49, 28-44 p.Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 49, 28-44 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Technology
Identifiers
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: diva2:787893
Funder
EU, FP7, Seventh Framework Programme
Available from: 2015-02-12 Created: 2015-02-12 Last updated: 2016-04-21Bibliographically approved

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CiteExportLink to record
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Citation style
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