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SIGMOD
2010
ACM

Non-homogeneous generalization in privacy preserving data publishing

8 years 11 months ago
Non-homogeneous generalization in privacy preserving data publishing
Most previous research on privacy-preserving data publishing, based on the k-anonymity model, has followed the simplistic approach of homogeneously giving the same generalized value in all quasi-identifiers within a partition. We observe that the anonymization error can be reduced if we follow a non-homogeneous generalization approach for groups of size larger than k. Such an approach would allow tuples within a partition to take different generalized quasi-identifier values. Anonymization following this model is not trivial, as its direct application can easily violate kanonymity. In addition, non-homogeneous generalization allows for additional types of attack, which should be considered in the process. We provide a methodology for verifying whether a nonhomogeneous generalization violates k-anonymity. Then, we propose a technique that generates a non-homogeneous generalization for a partition and show that its result satisfies k-anonymity, however by straightforwardly applying it, ...
Wai Kit Wong, Nikos Mamoulis, David Wai-Lok Cheung
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where SIGMOD
Authors Wai Kit Wong, Nikos Mamoulis, David Wai-Lok Cheung
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