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Australasian Conference on Information Security and Privacy

ACISP 2019: Information Security and Privacy pp 651–659Cite as

A Quantitative Study of Attribute Based Correlation in Micro-databases and Its Effects on Privacy

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Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11547))

Abstract

Preserving the privacy associated with publicly released micro-databases is an active area of research since an adversary can mine sensitive information about the database respondents from them. The work in this paper establishes a working model for quantitatively estimating the attribute based correlation present among multiple micro-databases. In this study, we have introduced an information-theoretic metric termed as Correlation Degree \((\rho )\) which estimates the amount of correlated information present among two micro-databases and accordingly assigns a cumulative score in the range [0, 1]. The design of our proposed metric is based on the fact that correlation among multiple datasets exists due to the presence of both overlapping and implicitly dependent attributes. We have also established a functional association between \(\rho \) and the general notion of privacy during the execution of an adversarial linking attack. Finally, we have empirically validated our work by estimating the value of \(\rho \) and the resulting privacy loss for the Adult micro-database on the backdrop of two well-established privacy preservation models.

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Author information

Authors and Affiliations

  1. ABV-Indian Institute of Information Technology and Management Gwalior, Gwalior, 474015, India

    Debanjan Sadhya

  2. Indian Institute of Information Technology Allahabad, Allahabad, 211015, India

    Bodhi Chakraborty

Authors
  1. Debanjan Sadhya
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  2. Bodhi Chakraborty
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Corresponding author

Correspondence to Debanjan Sadhya .

Editor information

Editors and Affiliations

  1. Massey University, Palmerston North, New Zealand

    Julian Jang-Jaccard

  2. University of Wollongong, Wollongong, NSW, Australia

    Fuchun Guo

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Sadhya, D., Chakraborty, B. (2019). A Quantitative Study of Attribute Based Correlation in Micro-databases and Its Effects on Privacy. In: Jang-Jaccard, J., Guo, F. (eds) Information Security and Privacy. ACISP 2019. Lecture Notes in Computer Science(), vol 11547. Springer, Cham. https://doi.org/10.1007/978-3-030-21548-4_37

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  • DOI: https://doi.org/10.1007/978-3-030-21548-4_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21547-7

  • Online ISBN: 978-3-030-21548-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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