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Conference Paper: Non-homogeneous generalization in privacy preserving data publishing

TitleNon-homogeneous generalization in privacy preserving data publishing
Authors
Keywordsanonymization
non-homogeneous generalization
privacy
Issue Date2010
PublisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://www.acm.org/sigmod
Citation
The 2010 International Conference on Management of Data (SIGMOD '10), Indianapolis, IN., 6-11 June 2010. In Proceedings of the ACM Conference on Management of Data, 2010, p. 747-758 How to Cite?
AbstractMost 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 non-homogeneous 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. © 2010 ACM.
Persistent Identifierhttp://hdl.handle.net/10722/129569
ISBN
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorWong, WKen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.contributor.authorCheung, DWLen_HK
dc.date.accessioned2010-12-23T08:39:22Z-
dc.date.available2010-12-23T08:39:22Z-
dc.date.issued2010en_HK
dc.identifier.citationThe 2010 International Conference on Management of Data (SIGMOD '10), Indianapolis, IN., 6-11 June 2010. In Proceedings of the ACM Conference on Management of Data, 2010, p. 747-758en_HK
dc.identifier.isbn978-1-4503-0032-2-
dc.identifier.issn0730-8078en_HK
dc.identifier.urihttp://hdl.handle.net/10722/129569-
dc.description.abstractMost 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 non-homogeneous 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. © 2010 ACM.en_HK
dc.languageengen_US
dc.publisherAssociation for Computing Machinery, Inc. The Journal's web site is located at http://www.acm.org/sigmoden_HK
dc.relation.ispartofProceedings of the ACM SIGMOD International Conference on Management of Dataen_HK
dc.rightsProceedings of the ACM Conference on Management of Data. Copyright © Association for Computing Machinery.-
dc.subjectanonymizationen_HK
dc.subjectnon-homogeneous generalizationen_HK
dc.subjectprivacyen_HK
dc.titleNon-homogeneous generalization in privacy preserving data publishingen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailMamoulis, N:nikos@cs.hku.hken_HK
dc.identifier.emailCheung, DWL:dcheung@cs.hku.hken_HK
dc.identifier.authorityMamoulis, N=rp00155en_HK
dc.identifier.authorityCheung, DWL=rp00101en_HK
dc.description.naturelink_to_OA_fulltext-
dc.identifier.doi10.1145/1807167.1807248en_HK
dc.identifier.scopuseid_2-s2.0-77954730854en_HK
dc.identifier.hkuros176422en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77954730854&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage747en_HK
dc.identifier.epage758en_HK
dc.publisher.placeUnited Statesen_HK
dc.description.otherThe 2010 International Conference on Management of Data (SIGMOD '10), Indianapolis, IN., 6-11 June 2010. In Proceedings of the ACM Conference on Management of Data, 2010, p. 747-758-
dc.identifier.scopusauthoridWong, WK=8835876000en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK
dc.identifier.scopusauthoridCheung, DWL=34567902600en_HK

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