Spherical microaggregation: Anonymizing sparse vector spaces

Daniel Abril, Guillermo Navarro-Arribas, Vicenҫ Torra

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

© 2014 Elsevier Ltd. All rights reserved. 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.
Original languageEnglish
Pages (from-to)28-44
JournalComputers and Security
Volume49
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • Anonymization
  • Data mining
  • Information loss
  • Privacy preserving
  • Sparse data 10.1016/j.cose.2014.11.005
  • Vector space

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