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Enhancing aggregation phase of microaggregation methods for interval disclosure risk minimization

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Abstract

Microaggregation is a masking mechanism to protect confidential data in a public release. This technique can produce a k-anonymous dataset where data records are partitioned into groups of at least k members. In each group, a representative centroid is computed by aggregating the group members and is published instead of the original records. In a conventional microaggregation algorithm, the centroids are computed based on simple arithmetic mean of group members. This naïve formulation does not consider the proximity of the published values to the original ones, so an intruder may be able to guess the original values. This paper proposes a disclosure-aware aggregation model, where published values are computed in a given distance from the original ones to attain a more protected and useful published dataset. Empirical results show the superiority of the proposed method in achieving a better trade-off point between disclosure risk and information loss in comparison with other similar anonymization techniques.

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Notes

  1. The measures are discussed in Sect. 2.3 with more details.

  2. For simplicity, we define \(Var(X)=\sigma ^2_X=1/n \sum _{i=1}^{n}(x_i-\mu _{X})^2\) where X is a set of n equally likely values \(x_i\) with \(\mu _{X}=Mean(X)\).

  3. We review some general purpose \(\textit{DR}\) and \(\textit{IL}\) measures only for continuous data type, which is addressed in this paper. The variants of the measures for other data types can be found in Hundepool et al. (2012).

  4. It is also known as identity disclosure or re-identification risk.

  5. Interval disclosure is a special case of attribute disclosure for continuous datasets.

  6. The heuristic can be simply extended to consider each attribute separately, however, our experiments show that there is no a significant improvement that justifies this additional cost.

  7. These methods are described in Sect. 3.

  8. Please note that in Table 2, MDAV-DA usually performs better for \(k=5\) than other aggregation levels for MDAV-DA.

  9. In fact, we select the trade-off point with closest but greater \(\textit{DR}\) than the value of MDAV-DA, to allow a more (potential) decrease of \(\textit{IL}\) for the methods.

  10. An illustrative example is presented in Fig. 1.

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Acknowledgments

This research is partially supported by ITRC (Iran Telecommunication Research Center) under Contract No. 12200/500.

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Authors and Affiliations

  1. Computer Engineering Department, Tarbiat Modares University, Tehran, Iran

    Saeed Jalili

  2. School of Engineering, Damghan University, Damghan, Iran

    Reza Mortazavi

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  1. Reza Mortazavi
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  2. Saeed Jalili
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Correspondence to Saeed Jalili.

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Responsible editor: Kristian Kersting.

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Mortazavi, R., Jalili, S. Enhancing aggregation phase of microaggregation methods for interval disclosure risk minimization. Data Min Knowl Disc 30, 605–639 (2016). https://doi.org/10.1007/s10618-015-0432-z

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