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MCP Proxy For Pii Pseudo Anonymization

@gbrigandi

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トランスポート

stdio

作者

gbrigandi

投稿者

gbrigandi

MCP Proxy For Pii Pseudo Anonymization について

概要はまだありません

#mcp-proxy#pii-pseudo-anonymization#data-security

MCP Proxy For Pii Pseudo Anonymization の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "conceal-database": {
      "command": "mcp-server-conceal",
      "args": [
        "--target-command",
        "python3",
        "--target-args",
        "database-server.py --host localhost",
        "--config",
        "/path/to/mcp-server-conceal.toml"
      ],
      "env": {
        "DATABASE_URL": "postgresql://localhost/mydb"
      }
    }
  }
}

MCP Proxy For Pii Pseudo Anonymization のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is Mcp Proxy For Pii Pseudo Anonymization?

An MCP proxy that pseudo-anonymizes personally identifiable information (PII) before data reaches external AI providers like Claude, ChatGPT, or Gemini. It detects PII in responses from your MCP server and replaces it with realistic, structurally preserved fake data, maintaining semantic meaning and data relationships for AI analysis.

How to use Mcp Proxy For Pii Pseudo Anonymization?

Download or build the binary (mcp-server-conceal), install Ollama if using LLM-based detection modes, create a TOML configuration file, then run it as a proxy with --target-command and --target-args pointing to your existing MCP server. For example: mcp-server-conceal --target-command python3 --target-args "my-mcp-server.py" --config mcp-server-conceal.toml.

Key features of Mcp Proxy For Pii Pseudo Anonymization

  • Three detection modes: regex only, LLM only, or combined regex+LLM
  • Configurable regex patterns for emails, phones, SSNs, credit cards, IPs, URLs
  • Faker-based generation with locale and seed for consistent pseudo-anonymization
  • SQLite mapping database with configurable retention days
  • LLM detection result caching for performance
  • Supports custom prompt templates for domain-specific PII

Use cases of Mcp Proxy For Pii Pseudo Anonymization

  • Proxying MCP servers used with Claude Desktop to protect user PII
  • Sanitizing database or API responses before sending to external AI assistants
  • Adding a privacy layer to existing MCP tools without modifying their code
  • Production environments requiring pseudo-anonymization over simple redaction

よくある質問

1

What is pseudo-anonymization and how is it different from redaction?

Pseudo-anonymization replaces real PII with realistic fake data that preserves structure (e.g., `john.smith@acme.com` becomes `mike.wilson@techcorp.com`), maintaining semantic meaning for AI analysis. Redaction removes the data entirely, losing context.

2

What dependencies are required?

For regex mode, no external dependencies. For LLM modes, you need Ollama running locally with a model like `llama3.2:3b`. Build dependencies require Rust ≥1.70.

3

Where is the mapping data stored?

Real-to-fake mappings are stored in a SQLite database at the path specified by `mapping.database_path` in the config. LLM detection cache can also be stored in a separate SQLite database.

4

What transport does this proxy use?

The proxy communicates via stdio, wrapping the target MCP server’s command. It does not add network transport; it forwards MCP messages between the AI client and the target server.

5

How are detection results kept consistent across restarts?

A seed set in `faker.seed` ensures the same real PII always maps to the same fake data. The mapping database persists across restarts, and retention days control how long mappings are kept.

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