Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Bottom-Up Generalization: A Data Mining Solution to Privacy Protection
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
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
Achieving anonymity via clustering
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Achieving k-anonymity by clustering in attribute hierarchical structures
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
A user-oriented anonymization mechanism for public data
DPM'10/SETOP'10 Proceedings of the 5th international Workshop on data privacy management, and 3rd international conference on Autonomous spontaneous security
Preserving privacy of moving objects via temporal clustering of spatio-temporal data streams
Proceedings of the 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS
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The key idea of our k-anonymity is to cluster the personal data based on the density which is measured by the k-Nearest-Neighbor (KNN) distance. We add a constraint that each cluster contains at least k records which is not the same as the traditional clustering methods, and provide an algorithm to come up with such a clustering. We also develop more appropriate metrics to measure the distance and information loss, which is suitable in both numeric and categorical attributes. Experiment results show that our algorithm causes significantly less information loss than previous proposed clustering algorithms.