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Syntactic Anonymisation of Shared Datasets in Resource Constrained Environments

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Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII

Part of the book series: Lecture Notes in Computer Science ((TLDKS,volume 11250))

Abstract

Resource constrained environments (RCEs) describe remote or rural developing world regions where missing specialised expertise, and computational processing power hinders data analytics operations. Outsourcing to third-party data analytics service providers offers a cost-effective management solution. However, a necessary pre-processing step is to anonymise the data before it is shared, to protect against privacy violations. Syntactic anonymisation algorithms (k-anonymisation, l-diversity, and t-closeness) are an attractive solution for RCEs because the generated data is not use case specific. These algorithms have however been shown to be NP-Hard, and as such need to be re-factored to run efficiently with limited processing power. In previous work [23], we presented a method of extending the standard k-anonymization and l-diversity algorithms, to satisfy both data utility and privacy. We used a multi-objective optimization scheme to minimise information loss and maximize privacy. Our results showed that the extended l-diverse algorithm incurs higher information losses than the extended k-anonymity algorithm, but offers better privacy in terms of protection against inferential disclosure. The additional information loss (7%) was negligible, and did not negatively affect data utility. As a further step, in this paper, we extend this result with a modified t-closeness algorithm based on the notion of clustering. The aim of this is to provide a performance-efficient algorithm that maintains the low information loss levels of our extended k-anonymisation and l-diversity algorithms, but also provides protection against skewness and similarity attacks.

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Author information

Authors and Affiliations

  1. Hasso-Plattner-Institute, Potsdam, Germany

    Anne V. D. M. Kayem & Christoph Meinel

  2. Department of Computer Science, University of Cape Town, Cape Town, South Africa

    C. T. Vester

Authors
  1. Anne V. D. M. Kayem
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  2. C. T. Vester
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  3. Christoph Meinel
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Corresponding author

Correspondence to Anne V. D. M. Kayem .

Editor information

Editors and Affiliations

  1. IRIT, Paul Sabatier University, Toulouse, France

    Abdelkader Hameurlain

  2. FAW, University of Linz, Linz, Austria

    Roland Wagner

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

    Sven Hartmann

  4. Victoria University of Wellington, Wellington, New Zealand

    Hui Ma

Appendix A Notations: Summary

Appendix A Notations: Summary

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Table 3. Notation
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Kayem, A.V.D.M., Vester, C.T., Meinel, C. (2018). Syntactic Anonymisation of Shared Datasets in Resource Constrained Environments. In: Hameurlain, A., Wagner, R., Hartmann, S., Ma, H. (eds) Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII. Lecture Notes in Computer Science(), vol 11250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58384-5_2

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  • DOI: https://doi.org/10.1007/978-3-662-58384-5_2

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