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

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Smart Computing and Informatics

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 77))

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Abstract

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.

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Authors and Affiliations

  1. Bapuji Institute of Engineering and Technology, Davangere, Karnataka, 577004, India

    K. Ashoka & B. Poornima

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  1. K. Ashoka
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  2. B. Poornima
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Correspondence to K. Ashoka .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, PVP Siddhartha Institute of Technology, Vijayawada, Andhra Pradesh, India

    Suresh Chandra Satapathy

  2. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

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Ashoka, K., Poornima, B. (2018). Mutual Correlation-based Optimal Slicing for Preserving Privacy in Data Publishing. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Computing and Informatics . Smart Innovation, Systems and Technologies, vol 77. Springer, Singapore. https://doi.org/10.1007/978-981-10-5544-7_58

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  • DOI: https://doi.org/10.1007/978-981-10-5544-7_58

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5543-0

  • Online ISBN: 978-981-10-5544-7

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