Improved histograms for selectivity estimation of range predicates
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Wavelet-based histograms for selectivity estimation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Self-tuning histograms: building histograms without looking at data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Multi-dimensional selectivity estimation using compressed histogram information
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Selectivity estimation using probabilistic models
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Dynamic Maintenance of Wavelet-Based Histograms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Selectivity Estimation Without the Attribute Value Independence Assumption
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Achieving k-anonymity privacy protection using generalization and suppression
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
The optimization of queries in relational databases
The optimization of queries in relational databases
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
Privacy-enhancing k-anonymization of customer data
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
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
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Injecting utility into anonymized datasets
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
(α, k)-anonymity: an enhanced k-anonymity model for privacy preserving data publishing
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A secure distributed framework for achieving k-anonymity
The VLDB Journal — The International Journal on Very Large Data Bases
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Consistent selectivity estimation via maximum entropy
The VLDB Journal — The International Journal on Very Large Data Bases
Hiding the presence of individuals from shared databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Optimal k-Anonymity with Flexible Generalization Schemes through Bottom-up Searching
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Data & Knowledge Engineering
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
K-anonymization as spatial indexing: toward scalable and incremental anonymization
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Fast data anonymization with low information loss
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Privacy skyline: privacy with multidimensional adversarial knowledge
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Privacy-MaxEnt: integrating background knowledge in privacy quantification
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Workload-aware anonymization techniques for large-scale datasets
ACM Transactions on Database Systems (TODS)
Resisting structural re-identification in anonymized social networks
Proceedings of the VLDB Endowment
Privacy-preserving anonymization of set-valued data
Proceedings of the VLDB Endowment
Anonymizing bipartite graph data using safe groupings
Proceedings of the VLDB Endowment
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Modeling and Integrating Background Knowledge in Data Anonymization
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Towards Trajectory Anonymization: a Generalization-Based Approach
Transactions on Data Privacy
IEEE Transactions on Knowledge and Data Engineering
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
δ-Presence without Complete World Knowledge
IEEE Transactions on Knowledge and Data Engineering
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Anonymization-based privacy protection ensures that data cannot be traced back to individuals. Researchers working in this area have proposed a wide variety of anonymization algorithms, many of which require a considerable number of database accesses. This is a problem of efficiency, especially when the released data is subject to visualization or when the algorithm needs to be run many times to get an acceptable ratio of privacy/utility. In this paper, we present two instant anonymization algorithms for the privacy metrics k-anonymity and ℓ-diversity. Proposed algorithms minimize the number of data accesses by utilizing the summary structure already maintained by the database management system for query selectivity. Experiments on real data sets show that in most cases our algorithm produces an optimal anonymization, and it requires a single scan of data as opposed to hundreds of scans required by the state-of-the-art algorithms.