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Hybrid microaggregation for privacy preserving data mining

  • Balkis Abidi
  • Sadok Ben Yahia
  • Charith Perera
Original Research
  • 22 Downloads

Abstract

k-Anonymity by microaggregation is one of the most commonly used anonymization techniques. This success is owe to the achievement of a worth of interest trade-off between information loss and identity disclosure risk. However, this method may have some drawbacks. On the disclosure limitation side, there is a lack of protection against attribute disclosure. On the data utility side, dealing with a real datasets is a challenging task to achieve. Indeed, the latter are characterized by their large number of attributes and the presence of noisy data, such that outliers or, even, data with missing values. Generating an anonymous individual data useful for data mining tasks, while decreasing the influence of noisy data is a compelling task to achieve. In this paper, we introduce a new microaggregation method, called HM-pfsom, based on fuzzy possibilistic clustering. Our proposed method operates through an hybrid manner. This means that the anonymization process is applied per block of similar data. Thus, we can help to decrease the information loss during the anonymization process. The HM-pfsom approach proposes to study the distribution of confidential attributes within each sub-dataset. Then, according to the latter distribution, the privacy parameter k is determined, in such a way to preserve the diversity of confidential attributes within the anonymized microdata. This allows to decrease the disclosure risk of confidential information.

Keywords

Hybrid micoaggregation Information loss Identity disclosure risk Attribute disclosure risk Fuzzy and possibilistic clustering 

Notes

References

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.LIPAH, Faculty of Sciences of TunisUniversity of El-ManarTunisTunisia
  2. 2.School of Computing ScienceNewcastle UniversityNewcastleUK

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