Achieving k-Anonymity for Associative Classification in Incremental-Data Scenarios

  • Bowonsak Seisungsittisunti
  • Juggapong Natwichai
Part of the Communications in Computer and Information Science book series (CCIS, volume 223)


When a data mining model is to be developed, one of the most important issues is preserving the privacy of the input data. In this paper, we address the problem of data transformation to preserve the privacy with regard to a data mining technique, associative classification, in an incremental-data scenario. We propose an incremental polynomial-time algorithm to transform the data to meet a privacy standard, i.e. k-Anonymity. While the transformation can still preserve the quality to build the associative classification model. The computational complexity of the proposed incremental algorithm ranges from O(n log n) to O( Δn) depending on the characteristic of increment data. The experiments have been conducted to evaluate the proposed work comparing with a non-incremental algorithm. From the experiment result, the proposed incremental algorithm is more efficient in every problem setting.


Execution Time Class Label Generalization Level Incremental Algorithm Order Change 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bowonsak Seisungsittisunti
    • 1
  • Juggapong Natwichai
    • 1
  1. 1.Computer Engineering Department, Faculty of EngineeringChiang Mai UniversityChiang MaiThailand

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