carnaval 0.1.1

Reversible PII anonymization framework with an encrypted vault

Carnaval

The art of the mask - hide the identity, keep the meaning.

Carnaval is an open-source Python framework for reversible PII anonymization. It masks sensitive entities in text documents before they are sent to a cloud LLM, then restores the original values in the structured response the LLM returns.

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The problem

You want to use a cloud LLM (Claude, GPT, Mistral, Gemini...) to process text documents - order acknowledgements, invoices, business emails, contracts - but those documents contain personal or confidential data that must never leave your infrastructure in clear text.

The solution

RAW DOCUMENT  ──▶  [ Carnaval ]  ──▶  MASKED DOCUMENT  ──▶  Cloud LLM
                                                                  │
FINAL DOCUMENT  ◀──  [ Carnaval ]  ◀──  JSON / XML response  ◀──┘

1. Before sending - sensitive entities are replaced with placeholders such as [PERSON_1], [EMAIL_2], [ORG]. The placeholder ↔ real-value mapping is stored in an encrypted local vault.
2. After the response - the original values are re-injected into the JSON or XML structure returned by the LLM.

No data ever leaves your machine in clear text, and the LLM still receives a coherent, structured document it can reason about.

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Key features

• Reversible - every masked entity maps to a unique placeholder; the mapping lives in an AES-256-GCM encrypted vault.
• Coherent - the same value always receives the same placeholder within a run, so the LLM can reason about cross-references.
• Local-first - no network calls to anonymize. The optional neural model runs on your own machine.
• 9 entity types - PERSON, ORGANIZATION, LOCATION, EMAIL, PHONE, IBAN, BIC, VAT, SIREN/SIRET, URL.
• Layered detection - regex recognizers, deny lists, bundled dictionaries (GeoNames cities, first names), and an optional zero-shot neural recognizer (GLiNER).
• Multilingual - 6 languages: French, English, German, Spanish, Italian, Portuguese.
• Business profiles - acknowledge, invoice, email, plus private per-client profiles kept out of version control.
• 8 output formats - TXT, JSON, JSONL, XML, CoNLL, HTML, encrypted vault, audit metadata - all produced in a single pass.
• CLI and library - use the anonymize.py / reinject.py scripts, or import carnaval directly into your Python code.

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Pipeline

Carnaval is built as 7 self-contained stages, each with a clear input → output contract:

TXT ──▶ S1 Intake ──▶ S2 Preprocess ──▶ S3 Detect ──▶ S4 Resolve ──▶ S5 Mask ──▶ S6 Output
        (read)        (language,         (recognizers)  (dedup,        (placeholders  (8 formats)
                       normalize)                        arbitration)   + vault)

JSON / XML ──▶ S7 Reinject ──▶ JSON / XML with original values restored

See Architecture for details on each stage.

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Installation

Requires Python 3.11+ (tested on 3.13).

git clone <repository-url>
cd carnaval

python -m venv .venv
# Windows PowerShell
.\.venv\Scripts\Activate.ps1
# Linux / macOS
source .venv/bin/activate

pip install -r requirements.txt

The neural recognizer (GLiNER) is included in requirements.txt. The model (~500 MB) is downloaded automatically on first use; afterwards Carnaval works fully offline. See the Installation guide for an offline / air-gapped setup.

Configure the vault password

cp .env.example .env

Then edit .env and set a strong secret (16 characters minimum, 32+ recommended):

CARNAVAL_VAULT_PASSWORD=a-strong-randomly-generated-secret

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Quickstart - CLI

# 1. Anonymize a document
python anonymize.py inbox/order.txt --profile acknowledge

# 2. Send outbox/txt/order_anonymise.txt to your LLM, collect a JSON response

# 3. Re-inject the real values into the LLM response
python reinject.py response.json --vault outbox/vault/order_vault.enc

anonymize.py produces, in one pass, all 8 output files under outbox/ (txt/, json/, jsonl/, xml/, conll/, html/, vault/, meta/).

Useful flags: --no-gliner (regex + deny lists only, faster), --gliner-threshold 0.6, --profile invoice, --private my_client, --console (human-readable logs).

---

Quickstart - Python API

from pathlib import Path
from carnaval.pipeline import run_anonymization

masked, written, config = run_anonymization(
    input_path=Path("inbox/order.txt"),
    outbox_dir=Path("outbox"),
    vault_password="a-strong-randomly-generated-secret",
    profile="acknowledge",
    use_gliner=True,
)

print(masked.anonymized_text)      # text with placeholders
print(masked.by_category)          # {'PERSON': 2, 'ORGANIZATION': 1, ...}
print(written.json_path)           # path to the JSON output

Re-injecting an LLM response:

from carnaval.core.vault import Vault
from carnaval.stages.s7_reinject import reinject_json_data

vault = Vault(password="a-strong-randomly-generated-secret",
              path="outbox/vault/order_vault.enc")
vault.load()

llm_response = {"supplier": "[ORG_1]", "contact": "[PERSON_1]"}
restored = reinject_json_data(llm_response, vault)
# {"supplier": "Globex Inc.", "contact": "Jane Doe"}

See the Quickstart and Reinjection wiki pages for more.

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Security

The placeholder ↔ value mapping is stored in an encrypted vault:

Property	Value
Symmetric cipher	AES-256-GCM (authenticated encryption)
Key derivation	PBKDF2-HMAC-SHA256, 600,000 iterations
Salt	16 random bytes per file
Nonce	16 random bytes per file
Integrity tag	16 bytes - any tampering is detected on read

Without the password, the vault is unreadable. Carnaval makes no outbound network calls once the GLiNER model has been downloaded, and its structured logger redacts sensitive keys by default. It supports GDPR-style pseudonymization (Article 4.5). See Vault and Security.

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Supported languages

French (FR), English (EN), German (DE), Spanish (ES), Italian (IT) and Portuguese (PT). The language is auto-detected; mixed-language documents are handled via in-text linguistic markers. See Multilingual.

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Project status

Carnaval is a functional proof of concept. Core anonymization, re-injection, the encrypted vault and the 8 output formats are implemented and covered by an extensive automated test suite.

Testing

pytest                       # full suite (skips slow neural tests)
pytest -m slow               # real GLiNER tests (downloads the model)
pytest --cov=src/carnaval    # with coverage

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Documentation

The complete reference lives in the project wiki:

• Home - overview and table of contents
• Installation
• Quickstart
• Architecture
• Vault and Security
• Profiles
• Recognizers
• Multilingual
• Output Formats
• Reinjection
• Troubleshooting
• Contributing

The original design notes are kept under docs/.

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Contributing

Contributions are welcome - see CONTRIBUTING.md and our Code of Conduct. Please use only fictitious entities (Acme Corp, Globex, Jane Doe, Springfield...) in public fixtures and examples.

Contact & Security

• General questions, conduct reports: [e-mail]
• Bug reports and feature requests: GitHub issues
• Security vulnerabilities: please do not open a public issue - see SECURITY.md for responsible disclosure.

Citation

If you use Carnaval in your work, please cite it via its archived DOI:

Patrice AUBERT. Carnaval: a reversible PII anonymization framework. 2026. DOI: 10.5281/zenodo.20219604

A machine-readable CITATION.cff is included - GitHub turns it into a "Cite this repository" button.

License

Carnaval is released under the Apache License 2.0. See LICENSE.