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Privacy-preserving record linkage using encrypted identifiers is now possible, even with errors. A new protocol based on Bloom filters offers comparable results to non-encrypted data and outperforms phonetic methods.

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Area of Science:

  • Computer Science
  • Bioinformatics
  • Data Science

Background:

  • Probabilistic record linkage is crucial for integrating diverse datasets when unique identifiers are absent.
  • Data privacy concerns necessitate the encryption of identifiers in many research applications.
  • Current methods for record linkage with encrypted data may face limitations.

Purpose of the Study:

  • To develop a novel protocol for privacy-preserving record linkage using encrypted identifiers.
  • To address the challenge of potential errors within encrypted identifiers during record linkage.
  • To provide a robust solution for secure data integration across multiple databases.

Main Methods:

  • A new protocol employing Bloom filters on q-grams of identifiers was developed.
  • The protocol enables record linkage with encrypted identifiers, accommodating identifier errors.
  • This approach focuses on efficient and secure comparison of encrypted data.

Main Results:

  • The developed protocol achieved linkage results comparable to those using non-encrypted identifiers.
  • Performance was superior when compared to traditional phonetic encoding methods.
  • Tests were validated on both simulated and real-world databases.

Conclusions:

  • A novel protocol for privacy-preserving record linkage with encrypted, error-tolerant identifiers has been successfully proposed.
  • The protocol demonstrates high efficacy and is computationally efficient.
  • This method offers a valuable tool for numerous applications requiring secure data integration.