Secure privacy-preserving record linkage system from re-identification attack.

Privacy-preserving record linkage (PPRL) technology, crucial for linking records across datasets while maintaining privacy, is susceptible to graph-based re-identification attacks. These attacks compromise privacy and pose significant risks, such as identity theft and financial fraud. This study pro...

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Main Authors: Sejong Lee, Yushin Kim, Yongseok Kwon, Sunghyun Cho
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0314486
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author Sejong Lee
Yushin Kim
Yongseok Kwon
Sunghyun Cho
author_facet Sejong Lee
Yushin Kim
Yongseok Kwon
Sunghyun Cho
author_sort Sejong Lee
collection DOAJ
description Privacy-preserving record linkage (PPRL) technology, crucial for linking records across datasets while maintaining privacy, is susceptible to graph-based re-identification attacks. These attacks compromise privacy and pose significant risks, such as identity theft and financial fraud. This study proposes a zero-relationship encoding scheme that minimizes the linkage between source and encoded records to enhance PPRL systems' resistance to re-identification attacks. Our method's efficacy was validated through simulations on the Titanic and North Carolina Voter Records (NCVR) datasets, demonstrating a substantial reduction in re-identification rates. Security analysis confirms that our zero-relationship encoding effectively preserves privacy against graph-based re-identification threats, improving PPRL technology's security.
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publisher Public Library of Science (PLoS)
record_format Article
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spelling doaj-art-e3436716389248d8ad7f8e7be7bfd4222025-08-20T02:59:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e031448610.1371/journal.pone.0314486Secure privacy-preserving record linkage system from re-identification attack.Sejong LeeYushin KimYongseok KwonSunghyun ChoPrivacy-preserving record linkage (PPRL) technology, crucial for linking records across datasets while maintaining privacy, is susceptible to graph-based re-identification attacks. These attacks compromise privacy and pose significant risks, such as identity theft and financial fraud. This study proposes a zero-relationship encoding scheme that minimizes the linkage between source and encoded records to enhance PPRL systems' resistance to re-identification attacks. Our method's efficacy was validated through simulations on the Titanic and North Carolina Voter Records (NCVR) datasets, demonstrating a substantial reduction in re-identification rates. Security analysis confirms that our zero-relationship encoding effectively preserves privacy against graph-based re-identification threats, improving PPRL technology's security.https://doi.org/10.1371/journal.pone.0314486
spellingShingle Sejong Lee
Yushin Kim
Yongseok Kwon
Sunghyun Cho
Secure privacy-preserving record linkage system from re-identification attack.
PLoS ONE
title Secure privacy-preserving record linkage system from re-identification attack.
title_full Secure privacy-preserving record linkage system from re-identification attack.
title_fullStr Secure privacy-preserving record linkage system from re-identification attack.
title_full_unstemmed Secure privacy-preserving record linkage system from re-identification attack.
title_short Secure privacy-preserving record linkage system from re-identification attack.
title_sort secure privacy preserving record linkage system from re identification attack
url https://doi.org/10.1371/journal.pone.0314486
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AT yongseokkwon secureprivacypreservingrecordlinkagesystemfromreidentificationattack
AT sunghyuncho secureprivacypreservingrecordlinkagesystemfromreidentificationattack