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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 | |
| 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 |
