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

Sejong Lee1, Yushin Kim1, Yongseok Kwon1

  • 1Department of Computer Science and Engineering, Major in Bio-Artificial Intelligence, Hanyang University, Ansan-si, Gyeonggi-do, South Korea.

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Summary
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This study introduces a zero-relationship encoding scheme to defend privacy-preserving record linkage (PPRL) systems against graph-based re-identification attacks. The new method significantly reduces re-identification risks, enhancing data security and privacy protection.

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Area of Science:

  • Computer Science
  • Information Security
  • Data Privacy

Background:

  • Privacy-preserving record linkage (PPRL) is essential for data analysis across datasets.
  • PPRL systems are vulnerable to sophisticated graph-based re-identification attacks.
  • Such attacks pose risks including identity theft and financial fraud.

Purpose of the Study:

  • To propose a novel zero-relationship encoding scheme.
  • To enhance the security and privacy of PPRL systems against re-identification threats.
  • To minimize the linkage between source and encoded records.

Main Methods:

  • Development of a zero-relationship encoding scheme.
  • Simulation-based validation using the Titanic and North Carolina Voter Records (NCVR) datasets.
  • Security analysis to assess resistance against graph-based re-identification attacks.

Main Results:

  • Demonstrated a substantial reduction in re-identification rates across tested datasets.
  • Validated the efficacy of the zero-relationship encoding scheme.
  • Confirmed improved privacy preservation capabilities of PPRL systems.

Conclusions:

  • The proposed zero-relationship encoding scheme effectively strengthens PPRL systems against graph-based re-identification attacks.
  • This approach significantly enhances data privacy and security in record linkage applications.
  • The method offers a promising solution for secure data sharing and analysis.