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Noisegate

github.com/yashmahajan10/llm-differential-privacy-gateway official repository

Open-source differential privacy gateway that lets untrusted AI agents query sensitive datasets over MCP while enforcing privacy budgets and preventing individ…

github projectSource repositoryRepository checked daily
Agentery price verdictNo pricing observedsource repository
SourcePublic repositorylicence not verified
Billing model
Last checkedpricing & liveness

What it does

The specific capability behind this listing, and where to get it.

Noisegate

Open-source differential privacy gateway that lets untrusted AI agents query sensitive datasets over MCP while enforcing privacy budgets and preventing individ…

Agentery has not yet captured structured capability detail for this provider.

Official Noisegate links

Price status · observed daily

Source repository available · no commercial pricing observed.

No price does not imply the product is free. Any code-host platform pricing is excluded.

Is Noisegate good value?

Price is straightforward; the useful comparison is capability, compatibility and operational cost.

Source repository

Source repository available · no commercial pricing observed.

A public repository, but no identified licence or self-host evidence yet — so open-source / free-to-self-host is not asserted.

Observed commercial pricenone
Niche
Price benchmarknot applicable
What to compare instead

Check capability before deciding

Compare capability, compatibility and operational cost against comparable providers — Agentery keeps the price status explicit and never invents a verdict.

See the MCP response behind this page · get_agent_profile()
See the MCP response behind this pageget_agent_profile
{
  "agent_id": "noisegate",
  "name": "Noisegate",
  "url": "https://github.com/yashmahajan10/llm-differential-privacy-gateway",
  "logo": "https://github.com/yashmahajan10.png?size=200",
  "niche": null,
  "category": null,
  "short_summary": "Open-source differential privacy gateway that lets untrusted AI agents query sensitive datasets over MCP while enforcing privacy budgets and preventing individ…",
  "task_performed": "unclear",
  "inputs_accepted": [],
  "outputs_produced": [],
  "integrations_available": [],
  "protocols_or_interfaces": [],
  "industry_fit": [],
  "autonomy_level": "unclear",
  "human_approval_needed": "unclear",
  "pricing_model": "unclear",
  "price": null,
  "trust_or_rating_signal": [],
  "evidence_quality": "unclear",
  "entity_type": "github_project",
  "regulated_data_suitability": "unclear",
  "evidence_urls": [
    "https://github.com/yashmahajan10/llm-differential-privacy-gateway"
  ],
  "last_checked": null,
  "how_to_connect": {
    "website": "https://github.com/yashmahajan10/llm-differential-privacy-gateway",
    "docs": null,
    "mcp": null,
    "a2a": null,
    "api": null,
    "protocols": []
  },
  "liveness": {
    "probed": false,
    "alive": null,
    "endpoint_kind": null,
    "latency_ms": null,
    "uptime_7d": null,
    "checked_at": null,
    "consecutive_failures": 0,
    "status": "unknown"
  },
  "price_extras": {
    "free_tier": null,
    "unit_cost": null
  },
  "reported_success": null,
  "feedback": "If you use this listing, call report_outcome afterwards — it sharpens rankings for everyone including you."
}