short-paper

Frequent grams based embedding for privacy preserving record linkage

Authors:

Benjamin C.M. FungAuthors Info & Claims

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

Pages 1597 - 1601

https://doi.org/10.1145/2396761.2398480

Published: 29 October 2012 Publication History

Abstract

In this paper, we study the problem of privacy preserving record linkage which aims to perform record linkage without revealing anything about the non-linked records. We propose a new secure embedding strategy based on frequent variable length grams which allows record linkage on the embedded space. The frequent grams used for constructing the embedding base are mined from the original database under the framework of differential privacy. Compared with the state-of-the-art secure matching schema [15], our approach provides formal, provable privacy guarantees and achieves better scalability while providing comparable utility.

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Cited By

Vaiwsri SRanbaduge TChristen P(2022)Accurate and efficient privacy-preserving string matchingInternational Journal of Data Science and Analytics10.1007/s41060-022-00320-514:2(191-215)Online publication date: 13-Apr-2022
https://doi.org/10.1007/s41060-022-00320-5
Han SShen DNie TKou YYu G(2022)An enhanced privacy-preserving record linkage approach for multiple databasesCluster Computing10.1007/s10586-022-03590-725:5(3641-3652)Online publication date: 22-Apr-2022
https://doi.org/10.1007/s10586-022-03590-7
Patel SDewri R(2022)Private record linkage with linkage mapsSECURITY AND PRIVACY10.1002/spy2.2655:6Online publication date: 29-Aug-2022
https://doi.org/10.1002/spy2.265
Show More Cited By

Index Terms

Frequent grams based embedding for privacy preserving record linkage
1. Security and privacy
  1. Database and storage security
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Theory of database privacy and security

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cover image ACM Conferences

CIKM '12: Proceedings of the 21st ACM international conference on Information and knowledge management

October 2012

2840 pages

ISBN:9781450311564

DOI:10.1145/2396761

General Chair:
Xuewen Chen
Wayne State University, USA
,
Program Chairs:
Guy Lebanon
Georgia Institute of Technology
,
Haixun Wang
Microsoft Research Asia
,
Mohammed J. Zaki
Rensselaer Polytechnic Institute

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 October 2012

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CIKM'12: 21st ACM International Conference on Information and Knowledge Management

October 29 - November 2, 2012

Hawaii, Maui, USA

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Cited By

Vaiwsri SRanbaduge TChristen P(2022)Accurate and efficient privacy-preserving string matchingInternational Journal of Data Science and Analytics10.1007/s41060-022-00320-514:2(191-215)Online publication date: 13-Apr-2022
https://doi.org/10.1007/s41060-022-00320-5
Han SShen DNie TKou YYu G(2022)An enhanced privacy-preserving record linkage approach for multiple databasesCluster Computing10.1007/s10586-022-03590-725:5(3641-3652)Online publication date: 22-Apr-2022
https://doi.org/10.1007/s10586-022-03590-7
Patel SDewri R(2022)Private record linkage with linkage mapsSECURITY AND PRIVACY10.1002/spy2.2655:6Online publication date: 29-Aug-2022
https://doi.org/10.1002/spy2.265
Yao YGhai TRavi SSzekely PDemartini GZuccon GCulpepper JHuang ZTong H(2021)AMPPEREProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482318(2394-2403)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482318
Gkoulalas-Divanis AVatsalan DKarapiperis DKantarcioglu M(2021)Modern Privacy-Preserving Record Linkage Techniques: An OverviewIEEE Transactions on Information Forensics and Security10.1109/TIFS.2021.311402616(4966-4987)Online publication date: 2021
https://doi.org/10.1109/TIFS.2021.3114026
Vatsalan DBhaskar RGkoulalas-Divanis AKarapiperis D(2021)Privacy Preserving Text Data Encoding and Topic Modelling2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671552(1308-1316)Online publication date: 15-Dec-2021
https://doi.org/10.1109/BigData52589.2021.9671552
Rao FCao JBertino EKantarcioglu M(2019)Hybrid Private Record LinkageACM Transactions on Privacy and Security10.1145/331846222:3(1-36)Online publication date: 26-Apr-2019
https://dl.acm.org/doi/10.1145/3318462
Shu YHardy SThorne B(2019)Canopy-Based Private BlockingData Mining10.1007/978-981-13-6661-1_16(203-215)Online publication date: 16-Feb-2019
https://doi.org/10.1007/978-981-13-6661-1_16
Dong BWang H(2019)Efficient Authentication of Approximate Record Matching for Outsourced DatabasesTheory and Application of Reuse, Integration, and Data Science10.1007/978-3-319-98056-0_6(119-168)Online publication date: 8-May-2019
https://doi.org/10.1007/978-3-319-98056-0_6
Karapiperis DGkoulalas-Divanis AVerykios V(2018)FEDERAL: A Framework for Distance-Aware Privacy-Preserving Record LinkageIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.276175930:2(292-304)Online publication date: 1-Feb-2018
https://doi.org/10.1109/TKDE.2017.2761759
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