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name: privacy-tests description: "Routes TensorFlow Privacy users who want membership inference analysis, privacy reports, membership inference callbacks, or secret-sharer exposure checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Privacy tests

Use this sub-skill when the user wants to measure memorization or privacy risk from model outputs.

Trigger phrases

• "membership inference"
• "privacy report"
• "attack results"
• "epsilon lower bound"
• "privacy callback"
• "secret sharer"
• "exposure"
• "memorization"

What this sub-skill covers

• AttackInputData, SlicingSpec, and AttackType
• run_attacks(), run_membership_probability_analysis(), and run_attack_on_keras_model()
• MembershipInferenceCallback and the keras_evaluation callback path
• AttackResults, privacy-report metadata, and pandas summaries
• privacy_report plotting/report helpers
• secret-sharer secret generation and exposure computation

What it does not cover

• DP training or optimizer selection -> ../training/
• privacy budget and epsilon/noise search -> ../privacy-accounting/
• DPQuery internals -> ../queries/
• fast clipping internals -> ../fast-clipping/

Read this before you act

• references/api-reference.md for attack inputs, outputs, and the bundled smoke pattern.
• references/troubleshooting.md for tiny-data, shape, and dependency failures.
• ../../references/install-and-scope.md for the minimum CPU runtime.

Typical workflow

1. Decide whether the user wants membership inference or secret-sharer exposure analysis.
2. Build the smallest valid AttackInputData or secret-sharer fixture.
3. Use the bundled smoke helper before trying a larger analysis.
4. If the user is asking for training guidance rather than analysis, route to training.

Bundled helpers

• scripts/tiny_mia_smoke.py runs a threshold membership-inference attack on synthetic losses.
• scripts/tiny_secret_sharer_smoke.py generates a toy secret set and computes exposure on synthetic perplexities.