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
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
Mondrian Multidimensional K-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
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Utility-based anonymization using local recoding
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies
IEEE Transactions on Knowledge and Data Engineering
Set-Expression Based Method for Effective Privacy Preservation
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
Using Anonymized Data for Classification
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
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Data distortion is inevitable in privacy-preserving data publication and a lot of quality metrics have been proposed to measure the quality of anonymous data, where information loss metrics are popularly used. Most of existing information loss metrics, however, are non-semantic and hence are limited in reflecting the data distortion. Thus, the utility of anonymous data based on these metrics is constrained. In this paper, we propose a novel semantic information loss metric SILM, which takes into account the correlation among attributes. This new metric can capture the distortion more precisely than the state of art information loss metrics especially for the scenario where strong correlations exist among attributes. We evaluated the effect of SILM on data quality in terms of the accuracy of aggregate query answering and classification. Comprehensive experiments demonstrate that SILM can help improve the quality of anonymous data much more especially if integrated with proper anonymization algorithms.