What Presidio misses for Canadian PII, and what I did about it

Arthur Jin — Thu, 12 Nov 2026 23:03:48 +0000

I'm Arthur, a systems engineer at Geek+ in Toronto, where I work on warehouse automation integrations. This post is about canpii, an open-source package I wrote that adds Canadian identifiers to Microsoft Presidio, a widely used open-source PII detector.

The problem, in one support ticket

Here is a made-up ticket. Every identifier in this post is synthetic.

Ticket from Jane Tremblay: the clinic rejected my health card, OHIP 1234-567-897 AB.
Please also update our business number to 123456782 RT 0001.

And one in French:

Bonjour, mon numéro d'assurance maladie est BOUF 9451 1419 et la pharmacie le refuse.

The first contains an Ontario health number (OHIP) and a CRA Business Number with a GST/HST program account. The second contains a Québec health insurance number (RAMQ).

I ran both through stock Presidio 2.2.366: the default recognizers, nothing added, with the small spaCy models for English and French, and a score threshold of 0.4. This is what it found in the English ticket:

PERSON        'Jane Tremblay'   0.85
PHONE_NUMBER  '1234-567-897'    0.40
DATE_TIME     '123456782'       0.85
US_PASSPORT   '123456782'       0.40

After anonymization:

Ticket from <PERSON>: the clinic rejected my health card, OHIP <PHONE_NUMBER> AB.
Please also update our business number to <DATE_TIME> RT 0001.

The health number is masked, but as a phone number, and its version code AB is left behind. The Business Number is masked as a date, and the program account RT 0001 is left behind. If your policy says "block health numbers" or "allow phone numbers", it does the wrong thing in both cases.

In the French ticket, stock Presidio found nothing. The RAMQ number went through unchanged.

Why Canadian identifiers are awkward

There is no single "Canadian health number". Each province issues its own:

Ontario: 10 digits plus an optional one- or two-letter version code.
Québec: 4 letters and 8 digits, with the holder's birth date embedded.
British Columbia: 10 digits starting with 9, with a MOD-11 check digit.
Alberta: 9 digits, with no published check digit.

So one entity needs several recognizers, and they differ in how much you can trust a match. A BC number that passes its checksum is strong evidence. A 9-digit Alberta number is just nine digits; only the words around it ("AHCIP", "health number") tell you what it is. Passport numbers (2 letters and 6 digits) and bank account numbers (7 to 12 digits) are in the same position: without context they look like order IDs.

Then there is French. The context words that raise a score have to exist in both languages: "carte santé", "assurance maladie", "permis de conduire", "numéro d'entreprise".

Context matching has its own trap. Presidio does ship a Canadian SIN recognizer (disabled by default), and one of its context words is "sin". Presidio's default matching is by substring, and "sin" is a substring of "bu*sin*ess". With that recognizer enabled:

Number on file: 130-692-544.             CA_SIN 0.5
Our business number is 130-692-544.      CA_SIN 0.85

The word "business" raises the SIN score from 0.5 to 0.85, as much as the word "SIN" itself does. I reported this upstream as presidio#2459. canpii's analyzer uses whole-word context matching, where the second sentence scores 0.5.

What canpii does

canpii adds five entities in Presidio's own style: CA_HEALTH_NUMBER (Ontario, Québec, BC, Alberta), CA_PASSPORT, CA_BN, CA_BANK_ACCOUNT and CA_DRIVER_LICENSE (Ontario, Québec, BC). Each recognizer has English and French context words, and validation where an official rule exists: the BC checksum, the RAMQ birth date, the CRA program identifiers. It also turns on Presidio's own SIN and postal code recognizers.

from presidio_anonymizer import AnonymizerEngine
from canpii import create_analyzer, get_preset

analyzer = create_analyzer(languages=["en", "fr"])
entities = get_preset("pipeda").entity_list()

tickets = [
    (
        "en",
        "Ticket from Jane Tremblay: the clinic rejected my health card, "
        "OHIP 1234-567-897 AB. Please also update our business number to 123456782 RT 0001.",
    ),
    ("fr", "Bonjour, mon numéro d'assurance maladie est BOUF 9451 1419 et la pharmacie le refuse."),
]
for language, text in tickets:
    results = analyzer.analyze(text, language=language, entities=entities)
    print(AnonymizerEngine().anonymize(text=text, analyzer_results=results).text)

Output:

Ticket from <PERSON>: the clinic rejected my health card, OHIP <CA_HEALTH_NUMBER>. Please also update our business number to <CA_BN>.
Bonjour, mon numéro d'assurance maladie est <CA_HEALTH_NUMBER> et la pharmacie le refuse.

The version code and the program account are now part of the match, and the French ticket is handled. pipeda is one of three presets (with phipa and quebec_law25): lists of entity types that are a starting point for a policy. They are not legal advice.

Numbers

The repository has a benchmark: 1,740 bilingual template sentences with 700 labeled occurrences of the five identifiers above, some with a context word and some without. I ran the positives through stock Presidio and through Presidio + canpii at threshold 0.4. Recall:

Entity	Gold spans	Stock: flagged as anything	canpii: right entity
CA_HEALTH_NUMBER	240	0.696	0.863
CA_PASSPORT	60	0.183	0.667
CA_BN	60	0.500	0.933
CA_BANK_ACCOUNT	160	0.506	0.625
CA_DRIVER_LICENSE	180	0.483	0.778
All five	700	0.537	0.776

"Flagged as anything" is the generous reading for stock Presidio: any part of the identifier tagged as any entity, as in the ticket above. It covers the whole identifier for only 0.360 of the spans.

The caveat matters more than the table. I wrote the template sentences, so this is a regression benchmark, not an estimate of accuracy on real text. Recall is also only half of it: on the same corpus canpii's precision ranges from 0.71 (bank numbers) to 1.00. The method and full tables are in benchmarks/RESULTS.md.

Where it falls short

No context, no match, for the weak patterns. All of canpii's misses in that table are sentences without a context word. There it found 0 of 20 passport numbers and 0 of 60 bank numbers. That is deliberate, because flagging every 7-digit number is worse, but it means free-form text will have misses.
Provinces are missing. Health cards for Saskatchewan, Manitoba and Nova Scotia, and driver's licences for Alberta and Manitoba, are not covered yet. Neither are New Brunswick, PEI, Newfoundland and Labrador or the territories.
The French needs a native review. The French context words and test sentences have not been reviewed by someone who writes Québec French every day.
The benchmark is synthetic. A more realistic corpus is an open issue.

Issues #1 to #8 cover these gaps. Each province is a self-contained recognizer, validator and tests, with a recipe in CONTRIBUTING.md. If you know your province's card format, that is a small pull request. Two rules: every format rule cites a source, and no real identifiers, ever.