Hybrid Private Record Linkage: Separating Differentially Private Synopses from Matching Records

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dc.contributor.ORCID0000-0001-6423-4533 (Kantarcioglu, M)
dc.contributor.authorRao, F. -Y
dc.contributor.authorCao, J.
dc.contributor.authorBertino, E.
dc.contributor.authorKantarcioglu, Murat
dc.contributor.utdAuthorKantarcioglu, Murat
dc.date.accessioned2020-02-25T23:03:55Z
dc.date.available2020-02-25T23:03:55Z
dc.date.issued2019-04-26
dc.descriptionDue to copyright restrictions and/or publisher's policy full text access from Treasures at UT Dallas is limited to current UTD affiliates (use the provided Link to Article).
dc.description.abstractPrivate record linkage protocols allow multiple parties to exchange matching records, which refer to the same entities or have similar values, while keeping the non-matching ones secret. Conventional protocols are based on computationally expensive cryptographic primitives and therefore do not scale. To address these scalability issues, hybrid protocols have been proposed that combine differential privacy techniques with secure multiparty computation techniques. However, a drawback of such protocols is that they disclose to the parties both the matching records and the differentially private synopses of the datasets involved in the linkage. Consequently, differential privacy is no longer always satisfied. To address this issue, we propose a novel framework that separates the private synopses from the matching records. The two parties do not access the synopses directly, but still use them to efficiently link records. We theoretically prove the security of our framework under the state-of-the-art privacy notion of differential privacy for record linkage (DPRL). In addition, we develop a simple but effective strategy for releasing private synopses. Extensive experimental results show that our framework is superior to the existing methods in terms of efficiency. © 2019 Association for Computing Machinery.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.identifier.bibliographicCitationRao, F. -Y, J. Cao, E. Bertino, and M. Kantarcioglu. 2019. "Hybrid private record linkage: Separating differentially private synopses from matching records." ACM Transactions on Privacy and Security 22(3): art. 15, doi: https://doi.org/10.1145/3318462
dc.identifier.issn2471-2566
dc.identifier.issue3
dc.identifier.urihttp://dx.doi.org/10.1145/3318462
dc.identifier.urihttps://hdl.handle.net/10735.1/7305
dc.identifier.volume22
dc.language.isoen
dc.publisherAssociation for Computing Machinery
dc.rights©2019 Association for Computing Machinery
dc.source.journalACM Transactions on Privacy and Security
dc.subjectPrivacy
dc.subjectRecord linkage
dc.subjectCryptography
dc.subjectComputer networks--Scalability
dc.subjectComputer networks--Security measures
dc.titleHybrid Private Record Linkage: Separating Differentially Private Synopses from Matching Records
dc.type.genrearticle

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