A hybrid data anonymization integrated with suppression for preserving privacy in mining multi party data

  • Deivanai P
  • Nayahi J
  • Kavitha V
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Abstract

In recent years of data mining applications, an effective technique to preserve privacy is to anonymize the dataset that include private information before being released for mining. Inorder to anonymize the dataset, manipulate its content so that the records adhere to k-anonymity. Two common manipulation techniques used to achieve k-anonymity of a dataset are generalization and suppression. However, generalization presents a major drawback as it requires a manually generated domain hierarchy taxonomy for every quasi identifier in the dataset on which k-anonymity has to be performed. In this paper, new method for achieving k-anonymity (based on suppression) called `kactus' is proposed. In this method, efficient multi-dimensional suppression is performed, i.e., values are suppressed only on certain records depending on other attribute values, without the need for manually-produced domain hierarchy trees. Thus, this method identify attributes that have less influence on the classification of the data records and suppress them if needed in order to comply with k-anonymity. The method was evaluated on several datasets to evaluate its accuracy as compared to other k-anonymity based methods. Anonymisation can be integrated with perturbation for privacy preservation in a multiparty environment.

Author-supplied keywords

  • Classification
  • Decision Trees
  • Privacy Preserving Data Mining
  • k-Anonymity

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