Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Anonymizing transaction databases for publication
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Privacy-preserving anonymization of set-valued data
Proceedings of the VLDB Endowment
Exact Knowledge Hiding through Database Extension
IEEE Transactions on Knowledge and Data Engineering
On the Anonymization of Sparse High-Dimensional Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
PCTA: privacy-constrained clustering-based transaction data anonymization
Proceedings of the 4th International Workshop on Privacy and Anonymity in the Information Society
Utility-preserving transaction data anonymization with low information loss
Expert Systems with Applications: An International Journal
Utility-guided Clustering-based Transaction Data Anonymization
Transactions on Data Privacy
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Publishing transaction data containing individuals' activities may risk privacy breaches, so the need for anonymizing such data before their release is increasingly recognized by organizations. Several approaches have been proposed recently to deal with this issue, but they are still inadequate for preserving both data utility and privacy. Some incur unnecessary information loss in order to protect data, while others allow sensitive inferences to be made on anonymized data. In this paper, we propose a novel approach that enhances both data utility and privacy protection in transaction data anonymization. We model potential inferences of individuals' identities and their associated sensitive transaction information as a set of implications, and we design an effective algorithm that is capable of anonymizing data to prevent these sensitive inferences with minimal data utility loss. Experiments using real-world data show that our approach outperforms the state-of-the-art method in terms of preserving both privacy and data utility.