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
Top-Down Specialization for Information and Privacy Preservation
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
\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
Anonymizing sequential releases
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
(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
Anatomy: simple and effective privacy preservation
VLDB '06 Proceedings of the 32nd international conference 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
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Maintaining K-Anonymity against Incremental Updates
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Anonymity for continuous data publishing
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Achieving k-anonymity by clustering in attribute hierarchical structures
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Secure anonymization for incremental datasets
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
Anonymization-based attacks in privacy-preserving data publishing
ACM Transactions on Database Systems (TODS)
Distributed Anonymization: Achieving Privacy for Both Data Subjects and Data Providers
Proceedings of the 23rd Annual IFIP WG 11.3 Working Conference on Data and Applications Security XXIII
(α, k)-anonymous data publishing
Journal of Intelligent Information Systems
An integrated framework for de-identifying unstructured medical data
Data & Knowledge Engineering
k-automorphism: a general framework for privacy preserving network publication
Proceedings of the VLDB Endowment
Privacy-preserving data publishing: A survey of recent developments
ACM Computing Surveys (CSUR)
On t-closeness with KL-divergence and semantic privacy
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part II
Limiting disclosure of sensitive data in sequential releases of databases
Information Sciences: an International Journal
An association probability based noise generation strategy for privacy protection in cloud computing
ICSOC'12 Proceedings of the 10th international conference on Service-Oriented Computing
Updating outsourced anatomized private databases
Proceedings of the 16th International Conference on Extending Database Technology
Anonymizing sequential releases under arbitrary updates
Proceedings of the Joint EDBT/ICDT 2013 Workshops
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Previous works about privacy preserving serial data publishing on dynamic databases have relied on unrealistic assumptions of the nature of dynamic databases. In many applications, some sensitive values changes freely while others never change. For example, in medical applications, the disease attribute changes with time when patients recover from one disease and develop another disease. However, patients do not recover from some diseases such as HIV. We call such diseases permanent sensitive values. To the best of our knowledge, none of the existing solutions handle these realistic issues. We propose a novel anonymization approach called HD-composition to solve the above problems. Extensive experiments with real data confirm our theoretical results.