Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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
IEEE Transactions on Knowledge and Data Engineering
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
A unified framework for protecting sensitive association rules in business collaboration
International Journal of Business Intelligence and Data Mining
Detecting dependencies in an anonymized dataset
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
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Privacy concerns on sensitive data are becoming indispensable in data publishing and knowledge discovering. The k-anonymization provides a way to protect the sensitivity without fabricating the data records. However, the anonymity can be breached by leveraging the associations between quasi-identifiers and sensitive attributes. In this paper, we model the possible privacy breaches as Q-S associations using association and dissociation rules. We enhance the common k-anonymization methods by evaluating the Q-S associations. Moreover, we develop a greedy algorithm for rule hiding in order to remove all the Q-S associations in every anonymity-group. Our method can not only protect data from the privacy breaches but also minimize the data loss. We also make a comparison between our method and one of the common k-anonymization strategies.