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
Protecting Sensitive Knowledge By Data Sanitization
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Practical privacy: the SuLQ framework
Proceedings of the twenty-fourth 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
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
Towards identity anonymization on graphs
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Privacy Preservation in the Publication of Trajectories
MDM '08 Proceedings of the The Ninth International Conference on Mobile Data Management
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
Privacy-preserving anonymization of set-valued data
Proceedings of the VLDB Endowment
On the Anonymization of Sparse High-Dimensional Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Hiding sensitive knowledge without side effects
Knowledge and Information Systems
Towards Preference-Constrained k-Anonymisation
Database Systems for Advanced Applications
Walking in the crowd: anonymizing trajectory data for pattern analysis
Proceedings of the 18th ACM conference on Information and knowledge management
Data mining with differential privacy
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and 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
Preventing range disclosure in k-anonymised data
Expert Systems with Applications: An International Journal
Local and global recoding methods for anonymizing set-valued data
The VLDB Journal — The International Journal on Very Large Data Bases
PCTA: privacy-constrained clustering-based transaction data anonymization
Proceedings of the 4th International Workshop on Privacy and Anonymity in the Information Society
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)"
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Publishing Search Logs—A Comparative Study of Privacy Guarantees
IEEE Transactions on Knowledge and Data Engineering
Association rule discovery with the train and test approach for heart disease prediction
IEEE Transactions on Information Technology in Biomedicine
Hi-index | 12.05 |
Transaction data record various information about individuals, including their purchases and diagnoses, and are increasingly published to support large-scale and low-cost studies in domains such as marketing and medicine. However, the dissemination of transaction data may lead to privacy breaches, as it allows an attacker to link an individual's record to their identity. Approaches that anonymize data by eliminating certain values in an individual's record or by replacing them with more general values have been proposed recently, but they often produce data of limited usefulness. This is because these approaches adopt value transformation strategies that do not guarantee data utility in intended applications and objective measures that may lead to excessive data distortion. In this paper, we propose a novel approach for anonymizing data in a way that satisfies data publishers' utility requirements and incurs low information loss. To achieve this, we introduce an accurate information loss measure and an effective anonymization algorithm that explores a large part of the problem space. An extensive experimental study, using click-stream and medical data, demonstrates that our approach permits many times more accurate query answering than the state-of-the-art methods, while it is comparable to them in terms of efficiency.