Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Using unknowns to prevent discovery of association rules
ACM SIGMOD Record
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
Protecting Sensitive Knowledge By Data Sanitization
ICDM '03 Proceedings of the Third IEEE International Conference on 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
A Border-Based Approach for Hiding Sensitive Frequent Itemsets
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
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
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
IEEE Transactions on Knowledge and Data Engineering
Hiding sensitive knowledge without side effects
Knowledge and Information Systems
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
Privacy-preserving data mining through knowledge model sharing
PinKDD'07 Proceedings of the 1st ACM SIGKDD international conference on Privacy, security, and trust in KDD
Association Rule Hiding for Data Mining
Association Rule Hiding for Data Mining
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
Revisiting sequential pattern hiding to enhance utility
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Association rule discovery with the train and test approach for heart disease prediction
IEEE Transactions on Information Technology in Biomedicine
An automated data utility clustering methodology using data constraint rules
Proceedings of the 2012 international workshop on Smart health and wellbeing
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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 they may incur significant utility loss due to their inability to: (i) accommodate a range of different privacy requirements that data owners often have, and (ii) guarantee that the produced data will satisfy data owners' utility requirements. To address this issue, we propose a novel clustering-based framework to anonymizing transaction data, which provides the basis for designing algorithms that better preserve data utility. Based on this framework, we develop two anonymization algorithms which explore a larger solution space than existing methods and can satisfy a wide range of privacy requirements. Additionally, the second algorithm allows the specification and enforcement of utility requirements, thereby ensuring that the anonymized data remain useful in intended tasks. Experiments with both benchmark and real medical datasets verify that our algorithms significantly outperform the current state-of-the-art algorithms in terms of data utility, while being comparable in terms of efficiency.