NDLI: Non-homogeneous generalization in privacy preserving data publishing

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Non-homogeneous generalization in privacy preserving data publishing

Content Provider	CiteSeerX
Author	Wong, W. K. Mamoulis, Nikos Cheung, David W.
Description	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 k-anonymity. 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, privacy can be compromised if the attacker knows the anonymization algorithm. Based on this, we propose a randomization method that prevents this type of attack and show that k-anonymity is not compromised by it. Nonhomogeneous generalization can be used on top of any existing partitioning approach to improve its utility. In addition, we show that a new partitioning technique tailored for non-homogeneous generalization can further improve quality. A thorough experimental evaluation demonstrates that our methodology greatly improves the utility of anonymized data in practice.
File Format	PDF
Language	English
Publisher Institution	In Proceedings of the 2010 international conference on Management of Data (SIGMOD
Access Restriction	Open
Subject Keyword	Result Satisfies K-anonymity Direct Application Non-homogeneous Generalization Approach Non-homogeneous Generalization K-anonymity Model Quasi-identifier Value Simplistic Approach Anonymization Algorithm Thorough Experimental Evaluation New Partitioning Technique Anonymization Error Privacy-preserving Data Publishing Randomization Method Generalized Value Additional Type Nonhomogeneous Generalization Data Publishing Partitioning Approach Previous Research
Content Type	Text
Resource Type	Article

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