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Detection Benchmarks

Security claims should be inspectable. We continuously test our open-source detection engine against standardized datasets to measure accuracy, precision, and recall.

Pseudonymization reduces exposure. It does not make data anonymous. For a privacy product, recall matters enormously because false negatives leak data to external APIs.

Current Benchmark Results

  • Engine Version: 1.0.0
  • Commit SHA: f8387dc
  • Date: September 6, 2026
  • Dataset: Enron / Presidio standard sets (Synthetic & Real)
  • Sample size: 10,000 sentences
  • Languages: English, Italian

| Entity | Precision | Recall | F1 Score | | :--- | :--- | :--- | :--- | | Email | 0.99 | 0.99 | 0.99 | | Person | 0.94 | 0.88 | 0.91 | | Location | 0.91 | 0.85 | 0.88 | | Organization | 0.90 | 0.82 | 0.86 | | Phone | 0.98 | 0.95 | 0.96 | | IP Address | 1.00 | 1.00 | 1.00 |

Methodology

  1. Precision: Out of all the entities the engine flagged as "Person", how many were actually people? (A low precision means high false positives, destroying useful non-sensitive text).
  2. Recall: Out of all the actual people in the text, how many did the engine successfully flag? (A low recall means high false negatives, leaking sensitive data).
  3. F1 Score: The harmonic mean of precision and recall.

Reproduction

You can reproduce these benchmarks yourself using our open-source repository.

git clone https://github.com/ma2za/pseudonymize.io
cd pseudonymize
make benchmark