Evaluating PPRL Vs Clear Text Linkage with Real-World Data

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Michael Jarrett
Brent Hills
Yinshan Zhao
Adrian Brown
Sean Randall
James Boyd
Anna Ferrante
Kimberlyn McGrail


Privacy-preserving Record Linkage (PPRL) is a record linkage technique that can increase the security of personal information. PPRL uses techniques of either hashing identifiers (where exact matches are required) or Blooming identifiers (where partial matches are of interest before they are provided for linkage.

Objectives and Approach
We use LinXmart software to evaluate performance of PPRL linkage compared to linkage using clear text identifiers. The test linkage dataset is one that is routinely linked (N=2,672,257) at our linkage centre. The population spine (N=8,440,442) includes a record for every person who has resided in British Columbia, Canada over the past 30 years. Weights were determined using LinXmart’s implementation of the Expectation Maximization (EM) algorithm. For both linkages, accepted links were the highest-weighted candidate link with a weight above the threshold suggested by EM estimation. We compare linkage rates and quality and differences in weight and threshold estimations between clear-text and PPRL linkages

Clear-text and PPRL methods resulted in 97% and 90% linkage rates, respectively. Approximately 67% of records in the linked datasets contained a nominally unique ID. Records with a unique ID linked at higher rates (>99% for both clear-text and PPRL) while the linkage rate for records missing the ID differed substantially (92% /70% for clear-text/PPRL). Comparing PPRL linkage to the clear-text linkage, we obtain F-measures of 0.99 and 0.80 for records with and without the unique ID, respectively.

Conclusion / Implications
Linkage performance may be attributable to differences in comparison operators between the two methods. Bloomed fields compared with Dice coefficient allow for partial matching but may not be as sensitive as clear-text string comparisons. Numerical comparisons in PPRL are exact matches while clear-text comparisons allow for more sophisticated matching. Further refinements in PPRL are being explored to improve these results.

Article Details

How to Cite
Jarrett, M., Hills, B., Zhao, Y., Brown, A., Randall, S., Boyd, J., Ferrante, A. and McGrail, K. (2020) “Evaluating PPRL Vs Clear Text Linkage with Real-World Data”, International Journal of Population Data Science, 5(5). doi: 10.23889/ijpds.v5i5.1542.

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