C4.5: programs for machine learning
C4.5: programs for machine learning
Generalization-based data mining in object-oriented databases using an object cube model
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
Data-Driven Discovery of Quantitative Rules in Relational Databases
IEEE Transactions on Knowledge and Data Engineering
On Changing Continuous Attributes into Ordered Discrete Attributes
EWSL '91 Proceedings of the European Working Session on Machine Learning
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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
Generalization and decision tree induction: efficient classification in data mining
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
An Association Thesaurus for Information Retrieval
An Association Thesaurus for Information Retrieval
Clustering intrusion detection alarms to support root cause analysis
ACM Transactions on Information and System Security (TISSEC)
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
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
Privacy Protection: p-Sensitive k-Anonymity Property
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
A crossover operator for the k- anonymity problem
Proceedings of the 8th annual conference on Genetic and evolutionary computation
(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
Preservation of proximity privacy in publishing numerical sensitive data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Automatic generation of concept hierarchies using WordNet
Expert Systems with Applications: An International Journal
(t, λ)-Uniqueness: Anonymity Management for Data Publication
ICIS '08 Proceedings of the Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008)
A framework for efficient data anonymization under privacy and accuracy constraints
ACM Transactions on Database Systems (TODS)
Improving inference through conceptual clustering
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
ChiMerge: discretization of numeric attributes
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
On-the-fly hierarchies for numerical attributes in data anonymization
SDM'10 Proceedings of the 7th VLDB conference on Secure data management
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Generalization hierarchies are frequently used in computer science, statistics, biology, bioinformatics, and other areas when less specific values are needed for data analysis. Generalization is also one of the most used disclosure control technique for anonymizing data. For numerical attributes, generalization is performed either by using existing predefined generalization hierarchies or a hierarchy-free model. Because hierarchy-free generalization is not suitable for anonymization in all possible scenarios, generalization hierarchies are of particular interest for data anonymization. Traditionally, these hierarchies were created by the data owner with help from the domain experts. But while it is feasible to construct a hierarchy of small size, the effort increases for hierarchies that have many levels. Therefore, new approaches of creating these numerical hierarchies involve their automatic/on-the-fly generation. In this paper we extend an existing method for creating on-the-fly generalization hierarchies, we present several existing information loss measures used to assess the quality of anonymized data, and we run a series of experiments that show that our new method improves over existing methods to automatically generate on-the-fly numerical generalization hierarchies.