Cryptography and data security
Cryptography and data security
Protecting Respondents' Identities in Microdata Release
IEEE Transactions on Knowledge and Data Engineering
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
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
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
On k-anonymity and the curse of dimensionality
VLDB '05 Proceedings of the 31st international conference on Very large data bases
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
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
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
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Towards optimal k-anonymization
Data & Knowledge Engineering
Injector: Mining Background Knowledge for Data Anonymization
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Efficient k-anonymization using clustering techniques
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Secure anonymization for incremental datasets
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
Privacy-preserving deletion to generalization-based anonymous database
Proceedings of the CUBE International Information Technology Conference
Generically extending anonymization algorithms to deal with successive queries
Proceedings of the 21st ACM international conference on Information and knowledge management
Incremental processing and indexing for k, e-anonymisation
International Journal of Information and Computer Security
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Although the k-anonymity and ℓ-diversity models have led to a number of valuable privacy-protecting techniques and algorithms, the existing solutions are currently limited to static data release. That is, it is assumed that a complete dataset is available at the time of data release. This assumption implies a significant shortcoming, as in many applications data collection is rather a continual process. Moreover, the assumption entails “one-time” data dissemination; thus, it does not adequately address today's strong demand for immediate and up-to-date information. In this paper, we consider incremental data dissemination, where a dataset is continuously incremented with new data. The key issue here is that the same data may be anonymized and published multiple times, each of the time in a different form. Thus, static anonymization (i.e., anonymization which does not consider previously released data) may enable various types of inference. In this paper, we identify such inference issues and discuss some prevention methods.