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生産性・共同作業 · SKILL.md

privacy-accounting

privacy-accounting

Routes TensorFlow Privacy users who want privacy budgets, epsilon/delta statements, or the repo's privacy accounting CLIs.

Independent GitHub snapshot · Automated classification · Original documentation governs

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GitHub

name: privacy-accounting description: "Routes TensorFlow Privacy users who want privacy budgets, epsilon/delta statements, or the repo's privacy accounting CLIs." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Privacy accounting

Use this sub-skill when the user wants to compute privacy budgets, find a noise multiplier, or explain the accounting assumptions behind a DP-SGD run.

Trigger phrases

  • "compute epsilon"
  • "find the noise multiplier"
  • "DP-SGD privacy statement"
  • "privacy budget"
  • "accountant type"
  • "user-level privacy"
  • "tree aggregation accounting"

What this sub-skill covers

  • compute_dp_sgd_privacy / compute_dp_sgd_privacy_statement
  • compute_noise_from_budget
  • the RDP and PLD accountant choices
  • tree-aggregation privacy accounting helpers
  • user-level privacy notes when max_examples_per_user matters

What it does not cover

  • training-loop integration -> ../training/
  • DPQuery internals -> ../queries/
  • attack analysis -> ../privacy-tests/
  • fast clipping internals -> ../fast-clipping/

Read this before you act

  • references/api-reference.md for the verified function signatures and the CLI flag map.
  • references/troubleshooting.md for missing-flag, delta, user-level, and accountant-selection failures.
  • ../../references/install-and-scope.md for the minimum runtime.

Typical workflow

  1. Determine whether the user needs an example-level or user-level statement.
  2. Choose RDP unless the user explicitly wants PLD.
  3. Use the bundled CLI helper when a command-line answer is enough.
  4. Use the API reference when the user wants to embed the calculation in code.
  5. If the user wants a tree-aggregation variant, read the tree-accounting notes before answering.

Bundled helpers

  • scripts/compute_dp_sgd_privacy.py mirrors the privacy-statement CLI with the same core parameters.
  • scripts/compute_noise_from_budget.py mirrors the inverse search CLI that solves for a noise multiplier.

Cross-links

  • ../training/ usually feeds the values that this sub-skill consumes.
  • ../queries/ owns the lower-level query math that eventually feeds some accounting paths.
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