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EVALUATION OF THE DISCLOSURE RISK OF MASKING METHODS DEALING WITH TEXTUAL ATTRIBUTES

  • Identification data

    Identifier: imarina:9298244
    Authors:
    Martinez, SergioSanchez, DavidValls, Aida
    Abstract:
    Record linkage methods evaluate the disclosure risk of revealing confidential information in anonymized datasets that are publicly distributed. Concretely, they measure the capacity of an intruder to link records in the original dataset with those in the masked one. In the past, masking and record linkage methods have been developed focused on numerical or ordinal data. Recently, motivated by the proliferation of textual information, some authors have proposed masking methods to anonymize textual data. Textual attributes should be interpreted according to their semantics, which makes them more difficult to manage and compare than numerical data. In this paper, we propose a new record linkage method specially tailored to accurately evaluate their disclosure risk. Our method, named Semantic Record Linkage, relies on the theory of semantic similarity and uses widely available ontologies to interpret the semantics of data and propose coherent record linkages. Test performed over a real dataset shows that a semantic record linkage method evaluates better the disclosure risk when compared with a non-semantic approach.
  • Others:

    Author, as appears in the article.: Martinez, Sergio; Sanchez, David; Valls, Aida
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Martinez Lluis, Sergio / Sánchez Ruenes, David / Valls Mateu, Aïda
    Keywords: Semantic similarity Record linkage Privacy protection Ontologies Disclosure risk
    Abstract: Record linkage methods evaluate the disclosure risk of revealing confidential information in anonymized datasets that are publicly distributed. Concretely, they measure the capacity of an intruder to link records in the original dataset with those in the masked one. In the past, masking and record linkage methods have been developed focused on numerical or ordinal data. Recently, motivated by the proliferation of textual information, some authors have proposed masking methods to anonymize textual data. Textual attributes should be interpreted according to their semantics, which makes them more difficult to manage and compare than numerical data. In this paper, we propose a new record linkage method specially tailored to accurately evaluate their disclosure risk. Our method, named Semantic Record Linkage, relies on the theory of semantic similarity and uses widely available ontologies to interpret the semantics of data and propose coherent record linkages. Test performed over a real dataset shows that a semantic record linkage method evaluates better the disclosure risk when compared with a non-semantic approach.
    Thematic Areas: Theoretical computer science Software Information systems Engenharias iv Engenharias iii Engenharias i Computer science, artificial intelligence Computational theory and mathematics Ciência da computação Automation & control systems
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: david.sanchez@urv.cat aida.valls@urv.cat sergio.martinezl@urv.cat
    Author identifier: 0000-0001-7275-7887 0000-0003-3616-7809 0000-0002-3941-5348
    Record's date: 2024-10-12
    Papper version: info:eu-repo/semantics/acceptedVersion
    Link to the original source: http://www.ijicic.org/contents.htm
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: International Journal Of Innovative Computing, Information And Control. 8 (7A): 4869-4882
    APA: Martinez, Sergio; Sanchez, David; Valls, Aida (2012). EVALUATION OF THE DISCLOSURE RISK OF MASKING METHODS DEALING WITH TEXTUAL ATTRIBUTES. International Journal Of Innovative Computing, Information And Control, 8(7A), 4869-4882
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2012
    Publication Type: Journal Publications
  • Keywords:

    Automation & Control Systems,Computational Theory and Mathematics,Computer Science, Artificial Intelligence,Information Systems,Software,Theoretical Computer Science
    Semantic similarity
    Record linkage
    Privacy protection
    Ontologies
    Disclosure risk
    Theoretical computer science
    Software
    Information systems
    Engenharias iv
    Engenharias iii
    Engenharias i
    Computer science, artificial intelligence
    Computational theory and mathematics
    Ciência da computação
    Automation & control systems
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