Anonymity for continuous data publishing
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
T-rotation: Multiple Publications of Privacy Preserving Data Sequence
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Privacy preserving serial data publishing by role composition
Proceedings of the VLDB Endowment
Continuous privacy preserving publishing of data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Privacy-Preserving Data Publishing
Foundations and Trends in Databases
Transparent anonymization: Thwarting adversaries who know the algorithm
ACM Transactions on Database Systems (TODS)
SABRE: a Sensitive Attribute Bucketization and REdistribution framework for t-closeness
The VLDB Journal — The International Journal on Very Large Data Bases
Limiting disclosure of sensitive data in sequential releases of databases
Information Sciences: an International Journal
Generically extending anonymization algorithms to deal with successive queries
Proceedings of the 21st ACM international conference on Information and knowledge management
Journal of Computer and System Sciences
Updating outsourced anatomized private databases
Proceedings of the 16th International Conference on Extending Database Technology
Incremental processing and indexing for k, e-anonymisation
International Journal of Information and Computer Security
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K-anonymity is a simple yet practical mechanismto protect privacy against attacks of re-identifying individuals by joining multiple public data sources. All existing methods achieving k-anonymity assume implicitly that the data objects to be anonymized are given once and fixed. However, in many applications, the real world data sources are dynamic. In this paper, we investigate the problem of maintaining k-anonymity against incremental updates, and propose a simple yet effective solution. We analyze how inferences from multiple releases may temper the k-anonymity of data, and propose the monotonic incremental anonymization property. The general idea is to progressively and consistently reduce the generalization granularity as incremental updates arrive. Our new approach guarantees the k-anonymity on each release, and also on the inferred table using multiple releases. At the same time, our new approach utilizes the more and more accumulated data to reduce the information loss.