Elements of information theory
Elements of information theory
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
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
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
\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
Anonymizing sequential releases
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
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Micro-aggregation-based heuristics for p-sensitive k-anonymity: one step beyond
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
Decomposition: Privacy Preservation for Multiple Sensitive Attributes
DASFAA '09 Proceedings of the 14th International Conference on Database Systems for Advanced Applications
Privacy protection on multiple sensitive attributes
ICICS'07 Proceedings of the 9th international conference on Information and communications security
Closeness: A New Privacy Measure for Data Publishing
IEEE Transactions on Knowledge and Data Engineering
Slicing: A New Approach for Privacy Preserving Data Publishing
IEEE Transactions on Knowledge and Data Engineering
Secure anonymization for incremental datasets
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
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Publishing individual specific microdata has serious privacy implications. The k-anonymity model has been proposed to prevent identity disclosure from microdata, and the work on l-diversity and t-closeness attempt to address attribute disclosure. However, most current work only deal with publishing microdata with a single sensitive attribute (SA), whereas real life scenarios often involve microdata with multiple SAs that may be multi-valued. This paper explores the issue of attribute disclosure in such scenarios. We propose a method called CODIP (Complete Disjoint Projections) that outlines a general solution to deal with the shortcomings in a naïve approach. We also introduce two measures, Association Loss Ratio and Information Exposure Ratio, to quantify data quality and privacy, respectively. We further propose a heuristic CODIP* for CODIP, which obtains a good trade-off in data quality and privacy. Finally, initial experiments show that CODIP* is practically useful on varying numbers of SAs.