Achieving k-anonymity for associative classification in incremental-data scenarios
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 s...
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| Main Authors: | , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2014
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| Online Access: | http://www.scopus.com/inward/record.url?eid=2-s2.0-80655143423&partnerID=40&md5=cdbbf534af0b53f8accf067bf9629cfe http://cmuir.cmu.ac.th/handle/6653943832/1538 |
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| Institution: | Chiang Mai University |
| Language: | English |
| Summary: | 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. © 2011 Springer-Verlag. |
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