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Composite Bloom Filters for Secure Record Linkage.

Elizabeth Ashley Durham1, Murat Kantarcioglu2, Yuan Xue3

  • 1Dept. of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232. ea.durham@vanderbilt.edu.

IEEE Transactions on Knowledge and Data Engineering
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Summary
This summary is machine-generated.

This study introduces a new private record linkage method using Bloom filters (BF) to enhance security against cryptanalysis. The statistically-informed approach balances security and accuracy for integrating sensitive data.

Keywords:
Bloom filterdata matchingentity resolutionprivacyrecord linkagesecurity

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Area of Science:

  • Computer Science
  • Data Privacy
  • Cryptography

Background:

  • Record linkage aims to integrate data from disparate sources but faces privacy challenges.
  • Traditional methods risk exposing sensitive identifiers.
  • Existing private record linkage (PRL) methods offer varying balances of accuracy, efficiency, and security.

Purpose of the Study:

  • To adapt Bloom filter (BF) encoding for private record linkage (PRL) to mitigate cryptanalysis.
  • To develop a method that minimizes sacrifices in accuracy and efficiency.
  • To enable a user-specified tradeoff between security and accuracy in data linkage.

Main Methods:

  • Developed a statistically-informed method for generating BF encodings.
  • Integrated bits from multiple fields, with frequencies provably linked to a minimum number of fields.
  • Compared the new encoding method against existing techniques using voter registration data.

Main Results:

  • The proposed BF encoding method enhances security against frequency-based cryptanalysis.
  • The technique allows for a user-defined balance between data security and linkage accuracy.
  • Empirical comparison showed only minor losses in accuracy with significant security gains.

Conclusions:

  • The novel BF encoding method offers improved security for private record linkage.
  • This approach provides a flexible way to manage the security-accuracy tradeoff.
  • The method is effective in real-world datasets, demonstrated by voter registration records.