depii

Detect and redact personally identifiable information (PII) in text, nested data, logs, files and DataFrames.

• No dependencies in the core. Pure Python, 3.10 and later.
• Validated, not just matched. Card numbers, IBANs, Aadhaar, NHS numbers and other identifiers are checked against their check digits, so a random run of digits is not flagged.
• Country packs for the United States, India, the United Kingdom, Germany, France, Spain, Italy, the Netherlands and the EU, plus a plug-in mechanism for more.
• Secrets too. API keys, tokens, private keys and password=... assignments.
• Several ways in: strings, nested dicts and lists, a logging filter, a command line tool, and pandas.

>>> import depii
>>> depii.redact("Mail jane@example.com or call +1 415 555 0132")
'Mail [EMAIL] or call [PHONE]'

Read this first

No tool finds every piece of PII, and this one does not either. Names, street addresses and free-form descriptions ("the tall man from flat 4") cannot be found by patterns at all. Treat the output as reduced-risk data, not as anonymous data.

Using this library does not by itself make a system compliant with GDPR, HIPAA, India's DPDP Act or any other law. Measure recall on a sample of your own data before you depend on it; Evaluation shows how.

Install

pip install depii

Optional extras: depii[pandas] for DataFrames, depii[ner] for names and places through spaCy, depii[phone] for stricter validation of international phone numbers, depii[all] for all three.

Usage

Text

>>> from depii import Redactor, Mask
>>> redactor = Redactor(policy={"CREDIT_CARD": Mask(keep_last=4)})
>>> result = redactor.redact("jane@example.com paid with 4111 1111 1111 1111")
>>> result.text
'[EMAIL] paid with **** **** **** 1111'
>>> result.stats
{'EMAIL': 1, 'CREDIT_CARD': 1}

Build a Redactor once and reuse it. It is immutable and safe to share between threads.

scan reports what was found without changing the text. The repr of a span leaves the matched value out, so spans can be logged:

>>> depii.scan("SSN 078-05-1120")
[Span(start=4, end=15, entity='US_SSN', score=1.0, detector='us_ssn')]

Options

Operators

Hash needs a secret key of at least 16 bytes and uses HMAC-SHA256. An unkeyed hash of a phone number or national ID can be reversed by trying every possible value, so it is not offered. Mask never reveals more than half of a value. A Vault holds the original values: protect it as you would the unredacted data.

Nested data

>>> depii.redact_obj({
...     "note": "call jane@example.com",
...     "user": {"firstName": "Jane", "password": "hunter2", "age": 41},
... })
{'note': 'call [EMAIL]', 'user': {'firstName': '[PERSON]', 'password': '[SECRET]', 'age': 41}}

Field names are used as hints. A field called password, ssn, userEmail or phone_number has its whole value redacted, even when the value does not look like PII. Pass key_hints=False to turn this off. The input object is not modified.

Logging

import logging
from depii import PIIFilter

handler = logging.StreamHandler()
handler.addFilter(PIIFilter())
logging.getLogger().addHandler(handler)

Attach the filter to handlers, not loggers: a filter on a logger does not see records that propagate up from child loggers. The message, its arguments, exception text, stack info and extra fields are all redacted. If redaction itself fails, the message is replaced with a fixed notice instead of being written unredacted.

Command line

depii redact notes.txt -o clean.txt
cat app.log | depii redact - > clean.log
depii redact events.jsonl --format jsonl -o clean.jsonl
depii redact people.csv --format csv --operator mask --keep-last 4 -o clean.csv
depii scan notes.txt
depii entities --locales us,in

scan prints file:line:column: ENTITY (score) for each finding and exits with status 1 when anything is found, so it can gate a commit or a CI job. It does not print the matched values unless you pass --show-values.

For --operator hash, the key is read from the environment variable DEPII_HASH_KEY, so that it does not appear in the process list or shell history.

Text input is streamed in chunks, so large files do not need to fit in memory. A file that is one single very long line is the exception.

pandas

from depii.frames import redact_dataframe

clean = redact_dataframe(df)  # every text column
clean = redact_dataframe(df, columns=["notes"])  # only these

Column names are used as hints in the same way as field names.

Names and places

Patterns cannot find names. The optional spaCy detector can, at the cost of a model download and slower runs:

pip install "depii[ner]"
python -m spacy download en_core_web_sm

from depii import Redactor
from depii.detectors.ner import SpacyDetector

redactor = Redactor(detectors=[SpacyDetector()])

It emits PERSON, LOCATION and ORGANIZATION. Its accuracy is that of the spaCy model you load.

Your own detectors

>>> from depii import PatternDetector, Redactor
>>> employee_id = PatternDetector(
...     "EMPLOYEE_ID",
...     r"\bE\d{5}\b",
...     score=0.4,
...     context=("employee", "staff"),
...     require_context=True,
... )
>>> Redactor(detectors=[employee_id]).redact("staff E12345 badge").text
'staff [EMPLOYEE_ID] badge'

A country pack is a module that exports DETECTORS. A separate package can register one under the depii.locales entry-point group, and it is then loaded like a built-in pack:

[project.entry-points."depii.locales"]
br = "depii_br:DETECTORS"

What is detected

Field-name hints can also produce PERSON, LOCATION, PASSPORT, BANK_ACCOUNT, NATIONAL_ID, TAX_ID and DRIVING_LICENCE.

Behaviour worth knowing

• Context words. Shapes that are too common to trust alone need a nearby word: a bare nine-digit number is an SSN only next to "SSN", a date is a date of birth only next to "born" or "DOB", and so on. strict=True removes this requirement.
• Context reaches across sentences. The window is about 48 characters before a value and 24 after. A second date shortly after "born 3 March 1985" is redacted as well.
• URLs. A URL is redacted when it carries credentials or a query string. A plain link is left alone unless strict=True.
• Aadhaar. A bare twelve-digit number starting with 2 to 9 that passes the Verhoeff check is treated as an Aadhaar number. About one in twelve arbitrary twelve-digit numbers does. If your data has no Indian identifiers, leave the in pack out.
• Labels can be ambiguous. A number that is valid as two things gets one label. It is redacted either way.
• Unicode. Full-width digits, non-Latin digits, unusual dashes and zero-width characters inside a value are handled. Internationalised email addresses are not.
• Not handled: images, PDFs, audio, and PII that is written out in words ("jane at example dot com").

Evaluation

python benchmarks/evaluate.py
python benchmarks/throughput.py

evaluate.py scores the detectors on a generated corpus: valid identifiers planted in sentences, plus numeric text that holds no PII. On that corpus every listed type currently reaches a recall and precision of 1.0. This shows that the detectors agree with the formats they claim to support. It does not predict recall on real text, which is messier than anything a generator produces. To measure on your own data, label a sample and pass it to synth.evaluate in tests/synth.py.

Throughput on one development laptop (Python 3.12, all packs loaded), for orientation only:

Loading fewer packs is the simplest way to go faster.

Development

python -m venv .venv
.venv/bin/pip install -e ".[dev]"     # .venv\Scripts\pip on Windows
ruff check . && ruff format --check .
mypy
pytest

The test suite includes property tests (Hypothesis) and a timing test that guards every pattern against catastrophic regex backtracking. Test data is generated: do not add real personal data to this repository.

Licence

Apache-2.0. See LICENSE.