Skip to main content

Offline document anonymizer for legal teams

Project description

anonymizer

Offline document anonymizer for legal teams. Replaces personally identifiable information (PII) in documents with structured tokens before sending them to external AI services.

Status: MVP-0 release candidate.

What it does

Drag a file (docx / pdf with text layer / xlsx) into the local web UI and get an anonymized document where:

  • Names, companies, financial details, addresses, emails, phones are replaced with structured tokens like [Person_1], [Company_1], [ADDRESS_1], ...
  • Document metadata is cleared
  • No network calls during processing — runs entirely on your machine

Then send the result to your AI tool of choice.

MVP-0 scope

  • Formats: docx, pdf with text layer, xlsx
  • Languages: Russian, English (NER); language-agnostic detectors for emails, phones, IBAN, cards, IP/MAC/URL, dates, geocoordinates
  • Platforms: Windows + macOS
  • UI: local web app at 127.0.0.1 in your browser
  • Install: single curl one-liner → uv tool install docs-anonymizer

OCR for scanned PDFs, password-protected files, additional languages — planned for later iterations (MVP-1+).

Installation

# macOS / Linux
curl -fsSL https://anonymizer.site/install.sh | sh

# Windows (PowerShell)
iwr -useb https://anonymizer.site/install.ps1 | iex

Then run anonymize — your browser will open at http://127.0.0.1:<port>.

Stack

Python 3.11+, FastAPI + htmx, spaCy + Natasha, PyMuPDF, python-docx, openpyxl, lxml. Full details in the technical spec.

Architecture

Three-layer design — core (headless Python library), cli, webapp (FastAPI on loopback) — plus testkit for synthetic test corpus generation and feedback loop tooling. Detectors are pluggable; language packs are drop-in. Manual masking + audit logging without PII leakage.

Licenses

The project is released under AGPL-3.0 because it depends on PyMuPDF (AGPL). All other dependencies are permissive open-source (MIT / Apache 2.0 / BSD / MPL). The source distribution published with each release contains the project source needed to satisfy AGPL source-availability obligations.

A page in the application UI will list all bundled libraries and models with their individual licenses.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

docs_anonymizer-0.2.18.tar.gz (726.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

docs_anonymizer-0.2.18-py3-none-any.whl (282.1 kB view details)

Uploaded Python 3

File details

Details for the file docs_anonymizer-0.2.18.tar.gz.

File metadata

  • Download URL: docs_anonymizer-0.2.18.tar.gz
  • Upload date:
  • Size: 726.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.9.5

File hashes

Hashes for docs_anonymizer-0.2.18.tar.gz
Algorithm Hash digest
SHA256 2454b394fa34c6c291d87c1984fed32a355a27736b5bed9ae85c130b17d7b550
MD5 931157c5d3e18cc0b46fc64c9e2660a5
BLAKE2b-256 0ba140a1a202708856c52ff1f22b3a776a524f85f7347d580173f9f781f46fc5

See more details on using hashes here.

File details

Details for the file docs_anonymizer-0.2.18-py3-none-any.whl.

File metadata

File hashes

Hashes for docs_anonymizer-0.2.18-py3-none-any.whl
Algorithm Hash digest
SHA256 3c2e28f8712419a19ea87485d622cd489f62148813d14213dd90daa24f7a7ff6
MD5 8de2cba3c90a53361853a025a6be0214
BLAKE2b-256 66509684b3a8a66fe890c4360798ef7fb1db44521639385b8bc50975ced34b93

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page