PCTA: privacy-constrained clustering-based transaction data anonymization
Proceedings of the 4th International Workshop on Privacy and Anonymity in the Information Society
A publication process model to enable privacy-aware data sharing
IBM Journal of Research and Development
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
A Knowledge Model Sharing Based Approach to Privacy-Preserving Data Mining
Transactions on Data Privacy
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Publishing transactional data about individuals in an anonymous form is increasingly required by organizations. Recent approaches ensure that potentially identifying information cannot be used to link published transactions to individuals’ identities. However, these approaches are inadequate to anonymize data that is both protected and practically useful in applications because they incorporate coarse privacy requirements, do not integrate utility requirements, and tend to explore a small portion of the solution space. In this paper, we propose the first approach for anonymizing transactional data under application-specific privacy and utility requirements. We model such requirements as constraints, investigate how these constraints can be specified, and propose COnstraint-based Anonymization of Transactions, an algorithm that anonymizes transactions using a flexible anonymization scheme to meet the specified constraints. Experiments with benchmark datasets verify that COAT significantly outperforms the current state-of-the-art algorithm in terms of data utility, while being comparable in terms of efficiency. Our approach is also shown to be effective in preserving both privacy and utility in a real-world scenario that requires disseminating patients’ information.