On Mining Sensitive Rules to Identify Privacy Threats View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2013

AUTHORS

Irene Díaz , Luis J. Rodríguez-Mũniz , Luigi Troiano

ABSTRACT

Data mining techniques represent a useful tool to cope with privacy problems. In this work an association rule mining algorithm adapted to the privacy context is developed. The algorithm produces association rules with a certain structure (the premise set is a subset of the public features of a released table while the consequent is the feature to protect). These rules are then used to reveal and explain relationships from data affected by some kind of anonymization process and thus, to detect threats. More... »

PAGES

232-241

References to SciGraph publications

  • 1988. Learning from Good and Bad Data in NONE
  • 2010. Identifying the Risk of Attribute Disclosure by Mining Fuzzy Rules in INFORMATION PROCESSING AND MANAGEMENT OF UNCERTAINTY IN KNOWLEDGE-BASED SYSTEMS. THEORY AND METHODS
  • 2008. k-Anonymous Data Mining: A Survey in PRIVACY-PRESERVING DATA MINING
  • Book

    TITLE

    Hybrid Artificial Intelligent Systems

    ISBN

    978-3-642-40845-8
    978-3-642-40846-5

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-40846-5_24

    DOI

    http://dx.doi.org/10.1007/978-3-642-40846-5_24

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1005034921


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    152 schema:name Department of Engineering, University of Sannio, Italy
    153 rdf:type schema:Organization
     




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