Generalizing data to provide anonymity when disclosing information (abstract)
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
A formal analysis of information disclosure in data exchange
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third 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
Checking for k-anonymity violation by views
VLDB '05 Proceedings of the 31st international conference on Very large data bases
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
Personalized privacy preservation
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Privacy in database publishing
ICDT'05 Proceedings of the 10th international conference on Database Theory
Publishing naive Bayesian classifiers: privacy without accuracy loss
Proceedings of the VLDB Endowment
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Among techniques for ensuring privacy in data publishing, k-anonymity and publishing of views on private data are quite popular. In this paper, we consider data publishing by views and develop a probability framework for the analysis of privacy breach. We propose two attack models and derive the probability of privacy breach for each model.