Privacy-preserving anonymization of set-valued data
Proceedings of the VLDB Endowment
Anonymizing bipartite graph data using safe groupings
Proceedings of the VLDB Endowment
Privacy protection for RFID data
Proceedings of the 2009 ACM symposium on Applied Computing
Anonymizing healthcare data: a case study on the blood transfusion service
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymizing location-based RFID data
C3S2E '09 Proceedings of the 2nd Canadian Conference on Computer Science and Software 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
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
Anonymizing bipartite graph data using safe groupings
The VLDB Journal — The International Journal on Very Large Data Bases
Algorithm-safe privacy-preserving data publishing
Proceedings of the 13th International Conference on Extending Database Technology
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
Centralized and Distributed Anonymization for High-Dimensional Healthcare Data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Anonymizing transaction data to eliminate sensitive inferences
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
Small domain randomization: same privacy, more utility
Proceedings of the VLDB Endowment
ρ-uncertainty: inference-proof transaction anonymization
Proceedings of the VLDB Endowment
Extended k-anonymity models against sensitive attribute disclosure
Computer Communications
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
C-safety: a framework for the anonymization of semantic trajectories
Transactions on Data Privacy
Publishing anonymous survey rating data
Data Mining and Knowledge Discovery
A publication process model to enable privacy-aware data sharing
IBM Journal of Research and Development
Anonymizing transaction data by integrating suppression and generalization
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Satisfying privacy requirements: one step before anonymization
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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
On the identity anonymization of high-dimensional rating data
Concurrency and Computation: Practice & Experience
Privacy preservation by disassociation
Proceedings of the VLDB Endowment
Anonymizing set-valued data by nonreciprocal recoding
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
PrivBasis: frequent itemset mining with differential privacy
Proceedings of the VLDB Endowment
Clustering-oriented privacy-preserving data publishing
Knowledge-Based Systems
Protecting User Privacy Better with Query l-Diversity
International Journal of Information Security and Privacy
Privacy-preserving trajectory data publishing by local suppression
Information Sciences: an International Journal
A new tool for sharing and querying of clinical documents modeled using HL7 Version 3 standard
Computer Methods and Programs in Biomedicine
A general framework for privacy preserving data publishing
Knowledge-Based Systems
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Existing research on privacy-preserving data publishing focuses on relational data: in this context, the objective is to enforce privacy-preserving paradigms, such as k-anonymity and l-diversity, while minimizing the information loss incurred in the anonymizing process (i.e. maximize data utility). However, existing techniques adopt an indexing-or clustering-based approach, and work well for fixed-schema data, with low dimensionality. Nevertheless, certain applications require privacy-preserving publishing of transaction data (or basket data), which involves hundreds or even thousands of dimensions, rendering existing methods unusable. We propose a novel anonymization method for sparse high-dimensional data. We employ a particular representation that captures the correlation in the underlying data, and facilitates the formation of anonymized groups with low information loss. We propose an efficient anonymization algorithm based on this representation. We show experimentally, using real-life datasets, that our method clearly outperforms existing state-of-the-art in terms of both data utility and computational overhead.