Decomposition+: Improving ℓ-Diversity for Multiple Sensitive Attributes View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2012

AUTHORS

Devayon Das , Dhruba K. Bhattacharyya

ABSTRACT

In this paper, we analyse existing privacy-transformation techniques in the field of PPDP that anonymize datasets with Multiple Sensitive Attributes (MSA). Of these, we present an analysis of Decomposition, an algorithm which generates a dataset with distinct ℓ-diversity over MSA using a partitioning approach. We discuss some improvements which can be made over Decomposition: in the realms of its running time, its data utility, and its applicability in the case of Multiple Release Publishing. To this effect, we describe Decomposition+ an algorithm that implements some of these improvements and is thus more suited for use in real-life scenarios. More... »

PAGES

403-412

References to SciGraph publications

  • 2006. Secure Anonymization for Incremental Datasets in SECURE DATA MANAGEMENT
  • 2009. Decomposition: Privacy Preservation for Multiple Sensitive Attributes in DATABASE SYSTEMS FOR ADVANCED APPLICATIONS
  • Book

    TITLE

    Advances in Computer Science and Information Technology. Computer Science and Engineering

    ISBN

    978-3-642-27307-0
    978-3-642-27308-7

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-27308-7_44

    DOI

    http://dx.doi.org/10.1007/978-3-642-27308-7_44

    DIMENSIONS

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