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

Sejong Lee, Yushin Kim, Yongseok Kwon and Sunghyun Cho

PLOS ONE, 2025, vol. 20, issue 1, 1-19

Abstract: 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.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0314486

DOI: 10.1371/journal.pone.0314486

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