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
k-anonymity: a model for protecting privacy
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
Ordinal, Continuous and Heterogeneous k-Anonymity Through Microaggregation
Data Mining and Knowledge Discovery
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Capturing data usefulness and privacy protection in K-anonymisation
Proceedings of the 2007 ACM symposium on Applied computing
Maintaining data privacy in association rule mining
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Data & Knowledge Engineering
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Robust De-anonymization of Large Sparse Datasets
SP '08 Proceedings of the 2008 IEEE Symposium on Security and Privacy
Anonymizing transaction databases for publication
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A Free Terrain Model for Trajectory K---Anonymity
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Privacy-preserving anonymization of set-valued data
Proceedings of the VLDB Endowment
Clustering
On the Anonymization of Sparse High-Dimensional Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
Efficient Multidimensional Suppression for K-Anonymity
IEEE Transactions on Knowledge and Data Engineering
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
Towards publishing recommendation data with predictive anonymization
ASIACCS '10 Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Anonymizing transaction data to eliminate sensitive inferences
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
ρ-uncertainty: inference-proof transaction anonymization
Proceedings of the VLDB Endowment
Local and global recoding methods for anonymizing set-valued data
The VLDB Journal — The International Journal on Very Large Data Bases
COAT: COnstraint-based anonymization of transactions
Knowledge and Information Systems - Special Issue on "Context-Aware Data Mining (CADM)"
Achieving k-anonymity by clustering in attribute hierarchical structures
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
ESORICS'05 Proceedings of the 10th European conference on Research in Computer Security
Utility-preserving transaction data anonymization with low information loss
Expert Systems with Applications: An International Journal
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Transaction data about individuals are increasingly collected to support a plethora of applications, spanning from marketing to biomedical studies. Publishing these data is required by many organizations, but may result in privacy breaches, if an attacker exploits potentially identifying information to link individuals to their records in the published data. Algorithms that prevent this threat by transforming transaction data prior to their release have been proposed recently, but incur significant information loss due to their inability to accommodate a range of different privacy requirements that data owners often have. To address this issue, we propose a novel clustering-based framework to anonymizing transaction data. Our framework provides the basis for designing algorithms that explore a larger solution space than existing methods, which allows publishing data with less information loss, and can satisfy a wide range of privacy requirements. Based on this framework, we develop PCTA, a generalization-based algorithm to construct anonymizations that incur a small amount of information loss under many different privacy requirements. Experiments with benchmark datasets verify that PCTA significantly outperforms the current state-of-the-art algorithms in terms of data utility, while being comparable in terms of efficiency.