Mutual Correlation-based Optimal Slicing for Preserving Privacy in Data Publishing

  • K. AshokaEmail author
  • B. Poornima
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 77)


Privacy preservation is a substantial concern for the organizations that publish/share personal data for informal analysis. Several anonymization algorithms such as generalization and Bucketization are developed as a solution to this Privacy Preserving Data Publishing (PPDP). Latest research has shown that generalization loses significant amount of information, particularly for high dimensional data. However, Bucketization does not prevent membership disclosure. In this paper, we propose a novel approach that makes use of Information Gain of the attributes with respect to sensitive attributes, which gives the effectiveness of an attribute in classifying the data, which is two-way association among attributes. We show that our approach preserves better data utility and has lesser complexity than earlier techniques. Our proposed technique is theoretically analyzed, and mathematical analysis outstrips past works with sufficient experiments.


Privacy preserving data Data anonymization Data perturbation Data utility 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Bapuji Institute of Engineering and TechnologyDavangereIndia

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