pseudonym-mcp

Local privacy proxy for LLMs — pseudonymizes sensitive data before it reaches the cloud, restores it on the way back.

Sits between your application and any cloud LLM (Claude, GPT-4, Gemini…). Replaces PII with opaque tokens locally before the prompt ever leaves your machine, then seamlessly restores original values in the response — so users never see the tags.

What you get

• Multi-language PII detection: Built-in support for English (SSN, credit cards, US phone) and Polish (PESEL, IBAN, Polish phone). Extensible to any language.
• Hybrid NER engine: Regex for structured PII (SSN, credit cards, IBAN, email, phone) + local Ollama LLM for unstructured entities (names, organizations).
• Zero-trust architecture: All detection and substitution happens on your machine. No PII reaches a third-party API.
• Session-keyed mapping store: Tokens like [PERSON:1] map back to originals in an isolated, per-request session. Multiple round-trips preserve token coherence.
• Auto-unmask: Optional mode that automatically restores tokens in the LLM's response before returning it to the user.
• Flexible engines: Run regex only (no Ollama required), llm only, or hybrid (default).
• Strict validation: SSN area-number validation, credit card Luhn checksum, PESEL checksum — all configurable.
• Graceful degradation: If Ollama is unavailable, the regex phase still runs and no exception is thrown.
• MCP-native: Works with Claude Code, Claude Desktop, Cursor — any MCP-compatible client.

❌ Without / ✅ With

❌ Without pseudonym-mcp:

• Prompt: "John Smith, SSN 123-45-6789, card 4111 1111 1111 1111" → sent verbatim to OpenAI / Anthropic servers
• Every name, ID number, and credit card in your prompt is processed and potentially logged by the LLM provider
• A data breach at the provider's end exposes your users' real PII
• Sending personal data to a US-based LLM provider without explicit safeguards may violate GDPR Article 44 (international data transfers)

✅ With pseudonym-mcp:

• The same prompt becomes "[PERSON:1], SSN [SSN:1], card [CREDIT_CARD:1]" before it leaves your machine
• The LLM reasons about the structure and content without ever seeing the real values
• The response is automatically de-tokenized locally before reaching the user
• Your GDPR DPA can truthfully state: personal data never left the local environment

GDPR & AI Compliance

pseudonym-mcp directly addresses the regulatory challenges of using cloud AI in data-sensitive contexts.

Why this matters

The EU General Data Protection Regulation (GDPR) classifies names, national ID numbers (like SSN or PESEL), bank account numbers (IBAN), email addresses, credit card numbers, and phone numbers as personal data under Article 4(1). Sending this data to a cloud LLM provider constitutes processing under Article 4(2) and triggers a range of obligations:

Note: Pseudonymisation under GDPR (Art. 4(5)) does not equal anonymisation — the data is still personal data in your system. However, it substantially reduces risk and demonstrates compliance with the accountability principle (Art. 5(2)).

AI Act alignment

The EU AI Act (in force from 2024) places additional requirements on high-risk AI systems that process personal data. Using pseudonym-mcp as an intermediary layer:

• Reduces the risk classification of downstream LLM usage by ensuring the model never processes identifiable natural persons' data directly.
• Supports documentation requirements for AI system transparency and human oversight.
• Aligns with the principle of technical robustness and safety (Art. 15) by limiting PII exposure surface.

US & international applicability

While GDPR originates in the EU, pseudonym-mcp is equally relevant for:

• CCPA / CPRA (California) — consumers have the right to know what personal information is collected; minimising data sent to third-party LLMs reduces disclosure surface.
• HIPAA (US healthcare) — PHI (Protected Health Information) must not be sent to non-BAA cloud providers; local pseudonymization allows LLM use without a BAA.
• PCI DSS (payment industry) — credit card numbers (PAN) must never be stored or transmitted in the clear; masking before LLM transit satisfies requirement 3.4.
• SOC 2 — data handling controls are strengthened by demonstrating that PII is replaced before leaving the trust boundary.
• PIPEDA (Canada), LGPD (Brazil), POPIA (South Africa) — all require appropriate safeguards for cross-border personal data transfers.

Sector-specific applicability

How it works

Your App / Claude Desktop
        │
        │  prompt with PII
        ▼
┌─────────────────────────┐
│      pseudonym-mcp      │
│                         │
│  Phase 1: Regex NER     │  ← SSN, CREDIT_CARD, EMAIL, PHONE (en)
│                         │  ← PESEL, IBAN, EMAIL, PHONE (pl)
│  Phase 2: Ollama NER    │  ← PERSON, ORG  (local LLM)
│  MappingStore (session) │  ← [TAG:N] ↔ original value
└────────────┬────────────┘
             │  sanitized prompt (no PII)
             ▼
      Cloud LLM API
      (Claude / GPT-4 / Gemini)
             │
             │  response with [TAG:N] tokens
             ▼
┌─────────────────────────┐
│      pseudonym-mcp      │
│   unmask_text / revert  │  ← tokens → originals
└────────────┬────────────┘
             │  restored response
             ▼
        Your App / User

Token format

English (--lang en, default):
[PERSON:1]       John Smith
[SSN:1]          123-45-6789
[CREDIT_CARD:1]  4111 1111 1111 1111
[ORG:1]          Acme Corp
[EMAIL:1]        john@acme.com
[PHONE:1]        (555) 123-4567

Polish (--lang pl):
[PERSON:1]       Jan Kowalski
[PESEL:1]        90010112318
[ORG:1]          Auto-Lux
[IBAN:1]         PL27114020040000300201355387
[EMAIL:1]        jan@example.pl
[PHONE:1]        +48 123 456 789

The mapping is stored in a session-scoped in-memory store. Each mask_text call returns a session_id; pass it back to unmask_text to restore originals.

Real-world example

Meeting note in Claude Code / Obsidian

You have a note:

Meeting with Jan Kowalski (PESEL: 90010112318) from Acme sp. z o.o.
We discussed a contract for 45 000 zł. Contact: jan.kowalski@acme.pl

In Claude Code you type:

Use mask_text on this note, then summarize the key points of the meeting.

pseudonym-mcp replaces PII locally before sending to Claude:

Meeting with [PERSON:1] ([PESEL:1]) from [ORG:1].
We discussed a contract for 45 000 zł. Contact: [EMAIL:1]

Claude responds (sees tokens only):

Meeting with [PERSON:1] from [ORG:1] covered a contract
for 45 000 zł. Follow up via [EMAIL:1].

pseudonym-mcp restores originals locally:

Meeting with Jan Kowalski from Acme sp. z o.o. covered
a contract for 45 000 zł. Follow up via jan.kowalski@acme.pl

Anthropic / OpenAI never saw any real data. The entire swap happens on your machine.

Obsidian vault with session_id

# mask the entire vault once — save the session_id
Use mask_text on my notes — remember the session_id

# ask Claude anything across multiple prompts
Summarize all meetings from Q1

# Claude replies with tokens; restore originals
Use unmask_text with session_id abc123 on the response

The session_id keeps the token map alive for the entire session — the same [PERSON:1] always refers to the same person, no matter how many times they appear across different notes.

MCP Prompt Templates

pseudonym-mcp ships two built-in prompt templates that chain masking, an LLM task, and unmasking into a single workflow — no glue code needed.

pseudonymize_task — inline text

/pseudonymize_task text="Meeting with Jan Kowalski (PESEL: 90010112318). Contract: 45 000 zł." task="Extract action items"

What happens:

1. pseudonym-mcp masks PII locally → [PERSON:1], [PESEL:1]
2. Claude processes the anonymized text
3. pseudonym-mcp restores originals in the response

Optional lang argument: en (default) or pl.

privacy_scan_file — file / PDF (macOS only)

Requires macos-vision-mcp — a separate MCP server that uses Apple's Vision framework to extract text from PDFs and images. macOS only.

/privacy_scan_file filePath="/Users/me/contracts/nda.pdf" task="Summarize obligations and deadlines"

What happens:

1. macos-vision-mcp extracts text from the file
2. pseudonym-mcp masks all PII locally
3. Claude processes the anonymized content
4. pseudonym-mcp restores originals before the response is shown

Optional arguments: task (default: summarize the key points), lang (en or pl).

Quick Start

Step 1 — Add to your MCP client (example for Claude Code — no install needed):

claude mcp add pseudonym-mcp -- npx -y pseudonym-mcp --engines hybrid

Step 2 — (Optional) Pull an Ollama model for full hybrid NER:

ollama pull llama3

Skip this step if you only need regex-based masking (--engines regex).

Global install — if you prefer npm install -g pseudonym-mcp, replace npx -y pseudonym-mcp with pseudonym-mcp in all snippets below.

Restart your client. The mask_text and unmask_text tools appear automatically.

Available Tools

mask_text input

{
  "text": "John Smith (SSN: 123-45-6789) works at Acme Corp.",
  "session_id": "optional — omit to create a new session"
}

mask_text output

{
  "session_id": "3f2a1b...",
  "masked_text": "[PERSON:1] (SSN: [SSN:1]) works at [ORG:1].",
  "auto_unmask": false
}

unmask_text input

{
  "text": "The case concerns [PERSON:1] at [ORG:1].",
  "session_id": "3f2a1b..."
}

Configuration

mcp-config.json (project root)

{
  "lang": "en",
  "engines": "hybrid",
  "ollamaModel": "llama3",
  "ollamaBaseUrl": "http://localhost:11434",
  "autoUnmask": false,
  "strictValidation": true
}

CLI flags

All config keys can be overridden at startup (highest priority):

pseudonym-mcp --lang en --engines regex --ollama-model llama3 --auto-unmask

Claude Code

claude mcp add pseudonym-mcp -- npx -y pseudonym-mcp --engines hybrid

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "pseudonym-mcp": {
      "command": "npx",
      "args": ["-y", "pseudonym-mcp", "--engines", "hybrid"]
    }
  }
}

Cursor

Add to ~/.cursor/mcp.json:

{
  "mcpServers": {
    "pseudonym-mcp": {
      "command": "npx",
      "args": ["-y", "pseudonym-mcp", "--engines", "regex"]
    }
  }
}

Supported PII types

English (--lang en, default)

Polish (--lang pl)

Engine modes

In hybrid mode, Ollama runs after the regex pass so the LLM never sees already-tokenized values. If Ollama is unreachable, the server logs a warning to stderr and returns the regex-only masked text — no crash, no hang.

Privacy & Security notes

• No telemetry. pseudonym-mcp makes no network requests except to your local Ollama instance and (optionally) the MCP stdio transport.
• In-memory only. The mapping store is never written to disk. Sessions are scoped to the server process lifetime.
• Idempotent tokens. The same original value always maps to the same token within a session ([PERSON:1] will not become [PERSON:2] for the same name on a second occurrence), preserving semantic coherence in LLM reasoning.
• No model training. The local Ollama model operates entirely offline. Your data is not used to train any model.
• Strict validation by default. Invalid SSNs (area 000/666/900+), failed-Luhn credit card numbers, and invalid-checksum PESELs are not masked, preventing false positives from OCR errors or random digit sequences.

Development

git clone https://github.com/woladi/pseudonym-mcp
cd pseudonym-mcp
npm install
npm run build    # tsc compile
npm test         # vitest (77 tests, no Ollama required)

The test suite runs fully offline — Ollama calls are injected via constructor and mocked in all tests. No live LLM required.

Adding a new language pack

1. Create src/languages/<lang>/rules.ts
2. Export an object that implements LanguageRules from src/languages/types.ts
3. Register it in the LANGUAGE_MAP in src/core/engine.ts
4. Pass --lang <lang> at startup

Each language pack defines an array of PatternDef entries with a tag, regex, and optional validate callback. See src/languages/en/rules.ts and src/languages/pl/rules.ts for examples.

Contributing

Contributions are welcome. Please follow Conventional Commits for commit messages — this project uses release-it with @release-it/conventional-changelog to automate releases.

Language pack contributions are especially welcome — German (Personalausweis, Steuer-ID), French (NIR, SIRET), Spanish (DNI/NIE) and others would significantly expand the tool's usefulness.

Keyword index

For discoverability: AI privacy, LLM data privacy, PII masking, PII redaction, PII detection, data pseudonymization, GDPR LLM compliance, GDPR AI, EU AI Act, CCPA compliance, HIPAA AI, PCI DSS tokenization, SOC 2 data handling, personal data protection, sensitive data scrubbing, NER anonymization, named entity recognition privacy, Claude privacy layer, MCP privacy proxy, local AI processing, on-premise AI, zero-trust AI, data minimisation, privacy by design, SSN masking, credit card masking, Luhn validation, PESEL masking, Polish PII, RODO, UODO compliance, healthcare AI privacy, financial data redaction, PSD2 privacy, tokenization NLP, prompt sanitization, context window privacy, offline NER, Ollama privacy, local LLM privacy, cross-border data transfer, data protection by design, PIPEDA, LGPD, POPIA.

License

MIT — Adrian Wolczuk