k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
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
A secure distributed framework for achieving k-anonymity
The VLDB Journal — The International Journal on Very Large Data Bases
L-diversity: Privacy beyond k-anonymity
ACM Transactions on Knowledge Discovery from Data (TKDD)
M-invariance: towards privacy preserving re-publication of dynamic datasets
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
k-Unlinkability: A privacy protection model for distributed data
Data & Knowledge Engineering
Minimality attack in privacy preserving data publishing
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Composition attacks and auxiliary information in data privacy
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Anonymization by Local Recoding in Data with Attribute Hierarchical Taxonomies
IEEE Transactions on Knowledge and Data Engineering
On Anti-Corruption Privacy Preserving Publication
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Versatile publishing for privacy preservation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Collusion-resistant privacy-preserving data mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Differentially private data release for data mining
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
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Data anonymization has become a major technique in privacy preserving data publishing. Many methods have been proposed to anonymize one dataset and a series of datasets of a data holder. However, no method has been proposed for the anonymization scenario of multiple independent data publishing. A data holder publishes a dataset, which contains overlapping population with other datasets published by other independent data holders. No existing methods are able to protect privacy in such multiple independent data publishing. In this paper we propose a new generalization principle (ρ,α)-anonymization that effectively overcomes the privacy concerns for multiple independent data publishing. We also develop an effective algorithm to achieve the (ρ,α)-anonymization. We experimentally show that the proposed algorithm anonymizes data to satisfy the privacy requirement and preserves high quality data utility.