Dynamic anonymization for marginal publication
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Permutation anonymization: improving anatomy for privacy preservation in data publication
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
AIM: a new privacy preservation algorithm for incomplete microdata based on anatomy
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
Anonymizing sequential releases under arbitrary updates
Proceedings of the Joint EDBT/ICDT 2013 Workshops
MAGE: A semantics retaining K-anonymization method for mixed data
Knowledge-Based Systems
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Generalization is a well-known method for privacy preserving data publication. Despite its vast popularity, it has several drawbacks such as heavy information loss, difficulty of supporting marginal publication, and so on. To overcome these drawbacks, we develop ANGEL,1 a new anonymization technique that is as effective as generalization in privacy protection, but is able to retain significantly more information in the microdata. ANGEL is applicable to any monotonic principles (e.g., l-diversity, t-closeness, etc.), with its superiority (in correlation preservation) especially obvious when tight privacy control must be enforced. We show that ANGEL lends itself elegantly to the hard problem of marginal publication. In particular, unlike generalization that can release only restricted marginals, our technique can be easily used to publish any marginals with strong privacy guarantees.