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Semantic Anonymisation of Categorical Datasets

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Book cover Advanced Research in Data Privacy

Part of the book series: Studies in Computational Intelligence ((SCI,volume 567))

Abstract

The exploitation of microdata compiled by statistical agencies is of great interest for the data mining community. However, such data often include sensitive information that can be directly or indirectly related to individuals. Hence, an appropriate anonymisation process is needed to minimise the risk of disclosing identities and/or confidential data. In the past, many anonymisation methods have been developed to deal with numerical data, but approaches tackling the anonymisation of non-numerical values (e.g. categorical, textual) are scarce and shallow. Since the utility of this kind of information is closely related to the preservation of its meaning, in this work, the notion of semantic similarity is used to enable a semantically coherent anonymisation. The knowledge modelled in ontologies is used as the basic pillar to propose semantic operators that enable an accurate management and transformation of categorical attributes. These operators are then used in three anonymisation mechanisms: Semantic Recoding, Semantic and Adaptive Microaggregation and Semantic Resampling. The three algorithms are compared in terms of semantic utility, privacy disclosure risk and runtime, with encouraging results.

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Acknowledgments

This work has been supported by the Spanish Ministry of Science and Innovation (through projects ICWT TIN2012-32757, ARES-CONSOLIDER INGENIO 2010 CSD2007-00004 and BallotNext IPT-2012-0603-430000) and by the Government of Catalonia under grants 2009 SGR 1135 and 2009 SGR-01523. Dr. Martínez was supported with research grants by the Universitat Rovira i Virgili and Ministerio de Educación y Ciencia (Spain).

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Authors and Affiliations

  1. Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, Avda. Països Catalans, 26, 43007, Tarragona, Catalonia, Spain

    Sergio Martínez, Aida Valls & David Sánchez

Authors
  1. Sergio Martínez
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  2. Aida Valls
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  3. David Sánchez
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Correspondence to Sergio Martínez .

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Editors and Affiliations

  1. Department of Information and Communications Engineering, Universitat Autonoma de Barcelona, Catalonia, Spain

    Guillermo Navarro-Arribas

  2. Consejo Superior de Investigaciones Científicas Campus de la UAB, Institut d'Investigació en Intel·ligència Artificial, Catalonia, Spain

    Vicenç Torra

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Martínez, S., Valls, A., Sánchez, D. (2015). Semantic Anonymisation of Categorical Datasets. In: Navarro-Arribas, G., Torra, V. (eds) Advanced Research in Data Privacy. Studies in Computational Intelligence, vol 567. Springer, Cham. https://doi.org/10.1007/978-3-319-09885-2_7

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  • DOI: https://doi.org/10.1007/978-3-319-09885-2_7

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-09885-2

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