Show HN: Noisegate – a differential-privacy gateway for untrusted AI agents
Noisegate is a new tool designed to provide differential privacy for AI agents interacting with sensitive data. It uses a validation layer to ensure that AI queries cannot leak individual records, even if the agent is compromised or adversarial.
Why it matters
As AI agents gain access to sensitive enterprise data, robust privacy enforcement layers are critical to prevent data breaches and unauthorized information exposure.
Give an AI agent query access to sensitive data, with a mathematical guarantee that no individual's record can leak — even if the agent is wrong, manipulated, or adversarial.
A recorded Claude Desktop session (replies trimmed; the chart cards are the session's own). An AI agent breaks 20 patients down by diagnosis, and the ±12 noise swamps every bin. Reminded that it cannot turn the noise off, it drains a three-answer budget until the gate returns a refusal instead of a quieter answer. On the 32,561-row census, a too-narrow slice is rejected at the trust boundary , while a full education breakdown comes back clean at scale. The refusal and the rejection are the live gateway's real enforcement, reproduced by python scripts/render_demo_gif.py . Try it from Use it from Claude Desktop .
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