Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
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
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and 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
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
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
A Novel Heuristic Algorithm for Privacy Preserving of Associative Classification
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Privacy preservation for associative classification: an approximation algorithm
International Journal of Business Intelligence and Data Mining
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Privacy preserving has become an essential process for any data mining task. In general, data transformation is needed to ensure privacy preservation. Once the privacy is preserved, data quality issue must be addressed, i.e. the impact on data quality should be minimized. In this paper, k-Anonymization is considered as the transformation approach for preserving data privacy. In such a context, we discuss the metrics of the data quality in terms of classification, which is one of the most important tasks in data mining. Since different type of classification may use different approach to deliver knowledge, data quality metric for the classification task should be tailored to a certain type of classification. Specifically, we propose a frequency-based data quality metric to represent the data quality of the transformed dataset in the situation that associative classification is to be processed. Subsequently, we validate our proposed metric with experiments. The experiment results have shown that our proposed metric can effectively reflect the data quality for the associative classification problem.