Typed, dependency-free, local-first PII pseudonymization for Python applications and LLM payloads.
Email paolo@example.com from 192.0.2.10.
↓
Email <EMAIL_1> from <IP_ADDRESS_1>.
Pseudonymize detects structured sensitive values locally and transforms them into numbered, generic, deterministic, or redacted tokens. The base package has no runtime dependencies, performs no telemetry or model downloads, and denies remote-capable backends by default.
Important
Pseudonymization is not anonymization. Detection can miss sensitive data, and transformed data can remain personal data. Review the security model and limitations before using the package with real data.
- Local by default. The standard-library core makes no network calls.
- Useful identity semantics. Repeated normalized values share an alias inside an explicit processing scope.
- Safe observability. Detailed reports expose types, offsets, provenance, and counts without copying matched values.
- Small installation. The wheel is typed and has zero base runtime dependencies.
- Explicit extension points. Detection and format handling are separate, so custom backends and adapters do not replace the core policy and transformation logic.
- Designed for LLM boundaries. Nested payload processing preserves structure and lets policies
include or exclude paths such as
messages.*.content.
| Capability | Status | Notes |
|---|---|---|
| Strings | Shipped | Pseudonymization, redaction, batch processing, and safe reports |
| Nested Python data | Shipped | Dictionaries, lists, tuples, JSON scalars, and path policies |
| Structured detection | Shipped | Email, phone, IP, IBAN, payment card, URL credentials, and common secrets |
| Document representation | Shipped | Immutable blocks, typed locations, sanitized metadata, and inspection |
| Generic file orchestration | Shipped | Built-in or caller-provided adapters and atomic safe-copy output |
| TXT, Markdown, log, JSON, JSONL, and CSV files | Shipped | Explicit format or recognized suffix; no content guessing |
| Names, organizations, and locations (PII) | Shipped | Requires the optional [ml] extra and a caller-provided local ONNX model |
python -m pip install pseudonymizePython 3.11 through 3.14 is supported.
The strict and exclusive goal of this package is PII pseudonymization. The optional ML backend is provided solely to identify unstructured personal data (names, organizations, locations) for pseudonymization, and must never be treated as a general-purpose NLP tool. To detect and pseudonymize this data, you can integrate a local ONNX model by installing the ml extra:
python -m pip install pseudonymize[ml]This installs onnxruntime, tokenizers, and numpy allowing you to configure the LocalONNXPIIBackend to run a lightweight local quantized DistilBERT model. No models are downloaded implicitly; you must provide your own paths to your downloaded model.onnx, tokenizer.json, and config.json.
from pseudonymize import pseudonymize, redact
safe = pseudonymize("Email paolo@example.com")
hidden = redact("Email paolo@example.com")
assert safe == "Email <EMAIL_1>"
assert hidden == "Email [REDACTED]"Numbered aliases are the default. Numbering starts from one for each convenience call.
from pseudonymize import Pseudonymizer
result = Pseudonymizer().process_with_report("Email paolo@example.com from 192.0.2.10.")
assert result.output == "Email <EMAIL_1> from <IP_ADDRESS_1>."
assert result.statistics.detections_found == 2
assert result.detections[0].backend == "rules"
assert "paolo@example.com" not in repr(result)Reports include entity type, block identifier, typed source location, relative offsets, confidence, detector, backend provenance, and an optional replacement token. They never include the matched value.
from pseudonymize import Policy, Pseudonymizer
payload = {
"model": "example-model",
"messages": [
{"role": "user", "content": "Email paolo@example.com"},
{"role": "user", "content": "Use paolo@example.com again"},
],
"temperature": 0.2,
}
result = Pseudonymizer(policy=Policy.llm()).process_data_with_report(payload)
assert result.output["messages"][0]["content"] == "Email <EMAIL_1>"
assert result.output["messages"][1]["content"] == "Use <EMAIL_1> again"
assert result.output["model"] == "example-model"The input is not mutated. Dictionary keys and non-string values are preserved.
| Mode | Example | Identity behavior |
|---|---|---|
numbered |
<EMAIL_1> |
Stable inside one explicit scope |
generic |
<EMAIL> |
Does not distinguish values of the same type |
deterministic |
<EMAIL_K8M42PX7D3Q> |
Stable for the same key, namespace, type, and normalized value |
redacted |
[REDACTED] |
Removes type and identity distinction |
from pseudonymize import Pseudonymizer
scope = Pseudonymizer().new_scope()
assert scope.process("paolo@example.com").text == "<EMAIL_1>"
assert scope.process("maria@example.com and paolo@example.com").text == ("<EMAIL_2> and <EMAIL_1>")Deterministic mode uses HMAC-SHA256 and requires a key of at least 32 bytes:
engine = Pseudonymizer(
mode="deterministic",
key=b"a-32-byte-or-longer-secret-key...",
namespace="customer-42",
)Different tenants should use different keys or namespaces. The package never generates, stores, or transmits a key silently.
Document contains immutable ContentBlock values. Each block has a stable identifier, text, a
typed source location, and immutable JSON-scalar metadata. Detection offsets remain relative to
the block text.
process_document() returns a transformed document. inspect_document() returns detections
without transformed output.
process_file() selects a built-in adapter from an explicit format or a recognized suffix. It
never overwrites the source, defaults to <stem>.safe<suffix>, refuses an existing destination
unless overwrite=True, and publishes rendered bytes atomically:
from pseudonymize import Pseudonymizer
result = Pseudonymizer().process_file("requests.json")
assert result.output.name == "requests.safe.json"
assert result.statistics.replacements_applied >= 0TXT, Markdown, log, JSON, JSONL, and strict comma-separated CSV files are dependency-free.
inspect_file() reports detections without writing output. Pass format="json" to override an
unknown suffix or use input_adapter and output_adapter for a custom format.
JSON, JSONL, and CSV outputs preserve data semantics but normalize insignificant whitespace,
quoting, and record endings. UTF-8 is strict by default, an existing UTF-8 BOM is preserved, and
an explicit codec can be supplied with encoding=.
The CLI exposes the same workflow:
pseudonymize file requests.json
pseudonymize inspect-file requests.jsonThe dependency-free RulesBackend handles structured values. A custom backend receives one
ContentBlock and the active Policy, then returns relative Detection offsets. Backends declare
supported entity types, provenance, remote capability, and remote-processing consent.
CompositeBackend merges leaf results through the same deterministic overlap resolver used by the
core. Malformed and out-of-range detections fail with sanitized exceptions.
No capitalization heuristic is used for names. PERSON, ORGANIZATION, and LOCATION are public
entity types, but the base package does not detect them.
NetworkPolicy.DENY is the default. A remote-capable backend is called only when both conditions
are true:
- The active policy is
ALLOW_CONFIGUREDwith the backend allowlisted, orALLOW_ALL. - The backend explicitly sets
allow_remote_processing=True.
An API key alone never enables network access. The current package defines this security contract but ships no remote provider or HTTP dependency.
Mappings are opt-in and available only in numbered and deterministic modes:
result = Pseudonymizer().process(
"paolo@example.com",
include_mapping=True,
)
assert result.restore("Reply to <EMAIL_1>.") == "Reply to paolo@example.com."Mappings contain sensitive source values. They are hidden from repr, never persisted by the
package, and must be protected separately by the application.
The dependency-free core API is stable throughout the 0.1 release line:
- documented public names, constructors, methods, CLI behavior, and token formats are preserved;
- patch releases preserve documented behavior and token formats;
- additive capabilities may appear in minor releases;
- a breaking core redesign requires an incompatible release;
- security fixes may reject unsafe inputs and are called out in release notes.
See the compatibility policy before upgrading the package in an application.
git clone https://github.com/ma2za/pseudonymize.git
cd pseudonymize
uv sync --all-extras --all-groups
uv run ruff format --check .
uv run ruff check .
uv run mypy
uv run pytest
uv run mkdocs build --strictTests must use synthetic values only. New capabilities require positive, negative, boundary, Unicode, adversarial, and cross-feature coverage where relevant.
Read CONTRIBUTING.md, SUPPORT.md, and SECURITY.md before opening an issue or pull request.
- Package: PyPI
- Changes: CHANGELOG.md
- Architecture: docs/architecture.md
- Vision: VISION.md
- Roadmap: ROADMAP.md
- Licence: Apache-2.0