@inproceedings{abril13:_towar_privat_vector_space_model_confid_docum,
  address =      {New York, {NY}, {USA}},
  series =       {{SAC} '13},
  title =        {Towards a private vector space model for confidential
                  documents},
  isbn =         {978-1-4503-1656-9},
  url =          {http://doi.acm.org/10.1145/2480486.2480543},
  doi =          {10.1145/2480362.2480543},
  abstract =     {We introduce in this paper a method to anonymize document
                  vector spaces. These vector spaces can be used to analyze
                  confidential documents without disclosing private information.
                  The method is inspired in microaggregation, a popular
                  technique used in statistical disclosure control.},
  urldate =      {2013-07-18},
  booktitle =    {Proceedings of the 28th Annual {ACM} Symposium on Applied
                  Computing},
  publisher =    {{ACM}},
  author =       {Abril, D. and Navarro-Arribas, G. and Torra, V.},
  year =         2013,
  keywords =     {Anonymization, document vector space, Indexes, privacy},
  pages =        {944–945},
}

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