SABRE: A Sensitive Attribute Bucketization and REdistribution framework for t-closeness

Handle URI:
http://hdl.handle.net/10754/561607
Title:
SABRE: A Sensitive Attribute Bucketization and REdistribution framework for t-closeness
Authors:
Cao, Jianneng; Karras, Panagiotis; Kalnis, Panos ( 0000-0002-5060-1360 ) ; Tan, Kianlee
Abstract:
Today, the publication of microdata poses a privacy threat: anonymous personal records can be re-identified using third data sources. Past research has tried to develop a concept of privacy guarantee that an anonymized data set should satisfy before publication, culminating in the notion of t-closeness. To satisfy t-closeness, the records in a data set need to be grouped into Equivalence Classes (ECs), such that each EC contains records of indistinguishable quasi-identifier values, and its local distribution of sensitive attribute (SA) values conforms to the global table distribution of SA values. However, despite this progress, previous research has not offered an anonymization algorithm tailored for t-closeness. In this paper, we cover this gap with SABRE, a SA Bucketization and REdistribution framework for t-closeness. SABRE first greedily partitions a table into buckets of similar SA values and then redistributes the tuples of each bucket into dynamically determined ECs. This approach is facilitated by a property of the Earth Mover's Distance (EMD) that we employ as a measure of distribution closeness: If the tuples in an EC are picked proportionally to the sizes of the buckets they hail from, then the EMD of that EC is tightly upper-bounded using localized upper bounds derived for each bucket. We prove that if the t-closeness constraint is properly obeyed during partitioning, then it is obeyed by the derived ECs too. We develop two instantiations of SABRE and extend it to a streaming environment. Our extensive experimental evaluation demonstrates that SABRE achieves information quality superior to schemes that merely applied algorithms tailored for other models to t-closeness, and can be much faster as well. © 2010 Springer-Verlag.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program
Publisher:
Springer Nature
Journal:
The VLDB Journal
Issue Date:
19-May-2010
DOI:
10.1007/s00778-010-0191-9
Type:
Article
ISSN:
10668888
Sponsors:
We thank Tiancheng Li and Ninghui Li from Purdue University for kindly providing us the implementations of tIncognito and tMondrian. This work is supported by two AcRF grants from Singapore's MOE. Jianneng Cao and Kian-Lee Tan are partially supported by grant T12-0702-P02. Panagiotis Karras is supported by grant T1 251RES0807.
Appears in Collections:
Articles; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorCao, Jiannengen
dc.contributor.authorKarras, Panagiotisen
dc.contributor.authorKalnis, Panosen
dc.contributor.authorTan, Kianleeen
dc.date.accessioned2015-08-02T09:15:14Zen
dc.date.available2015-08-02T09:15:14Zen
dc.date.issued2010-05-19en
dc.identifier.issn10668888en
dc.identifier.doi10.1007/s00778-010-0191-9en
dc.identifier.urihttp://hdl.handle.net/10754/561607en
dc.description.abstractToday, the publication of microdata poses a privacy threat: anonymous personal records can be re-identified using third data sources. Past research has tried to develop a concept of privacy guarantee that an anonymized data set should satisfy before publication, culminating in the notion of t-closeness. To satisfy t-closeness, the records in a data set need to be grouped into Equivalence Classes (ECs), such that each EC contains records of indistinguishable quasi-identifier values, and its local distribution of sensitive attribute (SA) values conforms to the global table distribution of SA values. However, despite this progress, previous research has not offered an anonymization algorithm tailored for t-closeness. In this paper, we cover this gap with SABRE, a SA Bucketization and REdistribution framework for t-closeness. SABRE first greedily partitions a table into buckets of similar SA values and then redistributes the tuples of each bucket into dynamically determined ECs. This approach is facilitated by a property of the Earth Mover's Distance (EMD) that we employ as a measure of distribution closeness: If the tuples in an EC are picked proportionally to the sizes of the buckets they hail from, then the EMD of that EC is tightly upper-bounded using localized upper bounds derived for each bucket. We prove that if the t-closeness constraint is properly obeyed during partitioning, then it is obeyed by the derived ECs too. We develop two instantiations of SABRE and extend it to a streaming environment. Our extensive experimental evaluation demonstrates that SABRE achieves information quality superior to schemes that merely applied algorithms tailored for other models to t-closeness, and can be much faster as well. © 2010 Springer-Verlag.en
dc.description.sponsorshipWe thank Tiancheng Li and Ninghui Li from Purdue University for kindly providing us the implementations of tIncognito and tMondrian. This work is supported by two AcRF grants from Singapore's MOE. Jianneng Cao and Kian-Lee Tan are partially supported by grant T12-0702-P02. Panagiotis Karras is supported by grant T1 251RES0807.en
dc.publisherSpringer Natureen
dc.subjectEarth Mover's Distanceen
dc.subjectt-closenessen
dc.titleSABRE: A Sensitive Attribute Bucketization and REdistribution framework for t-closenessen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.identifier.journalThe VLDB Journalen
dc.contributor.institutionSchool of Computing, National University of Singapore, Singapore, Singaporeen
kaust.authorKalnis, Panosen
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