Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Anonymizing sequential releases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
K-anonymization incremental maintenance and optimization techniques
Proceedings of the 2007 ACM symposium on Applied computing
Data Quality in Privacy Preservation for Associative Classification
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Achieving k-anonymity by clustering in attribute hierarchical structures
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Privacy preservation for associative classification: an approximation algorithm
International Journal of Business Intelligence and Data Mining
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Since individual data are being collected everywhere in the era of data explosion, privacy preserving has become a necessity for any data mining task. Therefore, data transformation to ensure privacy preservation is needed. Meanwhile, the transformed data must have quality to be used in the intended data mining task, i.e. the impact on the data quality with regard to the data mining task must be minimized. However, the data transformation problem to preserve the data privacy while minimizing the impact has been proven as an NP-hard. In this paper, we address the problem of maintaining the data quality in the scenarios which the transformed data will be used to build associative classification models. We propose a novel heuristic algorithm to preserve the privacy and maintain the data quality. Our heuristic is guided by the classification correction rate (CCR) of the given datasets. Our proposed algorithm is validated by experiments. From the experiments, the results show that the proposed algorithm is not only efficient, but also highly effective.