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Towards Identifying De-anonymisation Risks in Distributed Health Data Silos

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Database and Expert Systems Applications (DEXA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11706))

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Abstract

Accessing distributed and isolated data repositories such as medical research and treatment data in a privacy-preserving manner is a challenging problem. Furthermore, in the context of high-dimensional datasets, adhering to strict privacy legislation can be projected to a W[2]-complete problem whereby all privacy violating attribute combinations must be identified. While traditional anonymisation algorithms incur high levels of information loss when applied to high-dimensional data, they often do not guarantee privacy, which defeats the purpose of anonymisation. In this paper, we extend our previous work and address these issues by using Bayesian networks to handle data transformation for anonymisation [29]. By computing conditional probabilities linking attribute pairs for all attribute pair combinations the privacy exposure risk can be assessed. Attribute pairs differing by a high conditional probability indicate a high risk of de-anonymisation, similar to quasi-identifiers in syntactic anonymisation schemes, and can be separated instead of deleted. Attribute compartmentation removes the risk of privacy exposure, and deletion avoidance results in a significant reduction in information loss. In other words, assimilating the conditional probability of outliers directly in the adjacency matrix in a greedy fashion is efficient and privacy-preserving. Further, we offer deeper evaluation insights for optimising Bayesian networks with multigrid solver for aggregating state space explosion.

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Notes

  1. 1.

    https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-does-general-data-protection-regulation-gdpr-govern_en.

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Authors and Affiliations

  1. Hasso Plattner Institute, University of Potsdam, Potsdam, Germany

    Nikolai J. Podlesny, Anne V. D. M. Kayem & Christoph Meinel

Authors
  1. Nikolai J. Podlesny
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  2. Anne V. D. M. Kayem
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  3. Christoph Meinel
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Corresponding authors

Correspondence to Nikolai J. Podlesny , Anne V. D. M. Kayem or Christoph Meinel .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. The University of Texas at Arlington, Arlington, TX, USA

    Sharma Chakravarthy

  4. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Anderst-Kotsis

  5. Software Competence Center Hagenberg, Hagenberg im Mühlkreis, Austria

    A Min Tjoa

  6. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Podlesny, N.J., Kayem, A.V.D.M., Meinel, C. (2019). Towards Identifying De-anonymisation Risks in Distributed Health Data Silos. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11706. Springer, Cham. https://doi.org/10.1007/978-3-030-27615-7_3

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  • DOI: https://doi.org/10.1007/978-3-030-27615-7_3

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