File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A framework for efficient data anonymization under privacy and accuracy constraints

TitleA framework for efficient data anonymization under privacy and accuracy constraints
Authors
KeywordsAnonymity
Privacy
Issue Date2009
PublisherAssociation for Computing Machinery, Inc
Citation
ACM Transactions On Database Systems, 2009, v. 34 n. 2, article no. 9 How to Cite?
AbstractRecent research studied the problem of publishing microdata without revealing sensitive information, leading to the privacy-preserving paradigms of k-anonymity and l-diversity. k-anonymity protects against the identification of an individual's record. l-diversity, in addition, safeguards against the association of an individual with specific sensitive information. However, existing approaches suffer from at least one of the following drawbacks: (i) l-diversification is solved by techniques developed for the simpler k-anonymization problem, causing unnecessary information loss. (ii) The anonymization process is inefficient in terms of computational and I/O cost. (iii) Previous research focused exclusively on the privacy-constrained problem and ignored the equally important accuracy-constrained (or dual) anonymization problem. In this article, we propose a framework for efficient anonymization of microdata that addresses these deficiencies. First, we focus on one-dimensional (i.e., single-attribute) quasi-identifiers, and study the properties of optimal solutions under the k-anonymity and l-diversity models for the privacy-constrained (i.e., direct) and the accuracy-constrained (i.e., dual) anonymization problems. Guided by these properties, we develop efficient heuristics to solve the one-dimensional problems in linear time. Finally, we generalize our solutions to multidimensional quasi-identifiers using space-mapping techniques. Extensive experimental evaluation shows that our techniques clearly outperform the existing approaches in terms of execution time and information loss. © 2009 ACM.
Persistent Identifierhttp://hdl.handle.net/10722/60618
ISSN
2015 Impact Factor: 0.633
2015 SCImago Journal Rankings: 0.792
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorGhinita, Gen_HK
dc.contributor.authorKarras, Pen_HK
dc.contributor.authorKalnis, Pen_HK
dc.contributor.authorMamoulis, Nen_HK
dc.date.accessioned2010-05-31T04:15:05Z-
dc.date.available2010-05-31T04:15:05Z-
dc.date.issued2009en_HK
dc.identifier.citationACM Transactions On Database Systems, 2009, v. 34 n. 2, article no. 9en_HK
dc.identifier.issn0362-5915en_HK
dc.identifier.urihttp://hdl.handle.net/10722/60618-
dc.description.abstractRecent research studied the problem of publishing microdata without revealing sensitive information, leading to the privacy-preserving paradigms of k-anonymity and l-diversity. k-anonymity protects against the identification of an individual's record. l-diversity, in addition, safeguards against the association of an individual with specific sensitive information. However, existing approaches suffer from at least one of the following drawbacks: (i) l-diversification is solved by techniques developed for the simpler k-anonymization problem, causing unnecessary information loss. (ii) The anonymization process is inefficient in terms of computational and I/O cost. (iii) Previous research focused exclusively on the privacy-constrained problem and ignored the equally important accuracy-constrained (or dual) anonymization problem. In this article, we propose a framework for efficient anonymization of microdata that addresses these deficiencies. First, we focus on one-dimensional (i.e., single-attribute) quasi-identifiers, and study the properties of optimal solutions under the k-anonymity and l-diversity models for the privacy-constrained (i.e., direct) and the accuracy-constrained (i.e., dual) anonymization problems. Guided by these properties, we develop efficient heuristics to solve the one-dimensional problems in linear time. Finally, we generalize our solutions to multidimensional quasi-identifiers using space-mapping techniques. Extensive experimental evaluation shows that our techniques clearly outperform the existing approaches in terms of execution time and information loss. © 2009 ACM.en_HK
dc.languageengen_HK
dc.publisherAssociation for Computing Machinery, Incen_HK
dc.relation.ispartofACM Transactions on Database Systemsen_HK
dc.rightsACM Transactions on Database Systems. Copyright © Association for Computing Machinery, Inc.en_HK
dc.subjectAnonymityen_HK
dc.subjectPrivacyen_HK
dc.titleA framework for efficient data anonymization under privacy and accuracy constraintsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0362-5915&volume=34&issue=2&spage=47&epage=&date=2009&atitle=A+Framework+for+Efficient+Data+Anonymization+under+Privacy+and+Accuracy+Constraintsen_HK
dc.identifier.emailMamoulis, N:nikos@cs.hku.hken_HK
dc.identifier.authorityMamoulis, N=rp00155en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1145/1538909.1538911en_HK
dc.identifier.scopuseid_2-s2.0-68649129111en_HK
dc.identifier.hkuros166338en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-68649129111&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume34en_HK
dc.identifier.issue2en_HK
dc.identifier.spagearticle no. 9-
dc.identifier.epagearticle no. 9-
dc.identifier.isiWOS:000268472600002-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridGhinita, G=22834343300en_HK
dc.identifier.scopusauthoridKarras, P=14028488200en_HK
dc.identifier.scopusauthoridKalnis, P=6603477534en_HK
dc.identifier.scopusauthoridMamoulis, N=6701782749en_HK

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats