Testing whether counts and summaries respect hidden-record visibility
この記事はまだ日本語では提供されていません。原文を表示しています。
Check whether derived responses follow the product’s visibility rules for protected records. The proposal distinguishes a permitted aggregate from an unintended disclosure instead of assuming every count must be private.
Goal
Check whether derived responses follow the product’s visibility rules for protected records. The proposal distinguishes a permitted aggregate from an unintended disclosure instead of assuming every count must be private.
Prerequisites
Create an isolated synthetic dataset with public and restricted records. Define which counts, facets, badges, summaries, and existence signals each test principal is permitted to observe.
Steps
-
Collect the declared aggregate through an authorized account and confirm the fixture’s composition. Use deterministic synthetic categories so changes can be attributed to a known record.
-
Query the same feature as a less-privileged account. Compare its output with the policy-defined visible dataset, not with the unrestricted account’s response by default.
-
Add one restricted synthetic record while keeping public records unchanged. Repeat the lower-privilege query and evaluate whether any changed count or category is allowed by the aggregate policy.
-
Repeat for alternate presentations actually supported by the application, such as a search facet or navigation badge. Name each presentation so a fix in one handler does not conceal another result.
-
After repair, check that authorized aggregates remain correct and that public record changes still update permitted summaries. Avoid a blanket constant response that merely hides a functional regression.
Expected result
The regression should identify which derived value changes with protected data and whether that change violates an explicitly stated disclosure policy.
Limits and test basis
This is a deterministic application-level comparison, not a statistical privacy guarantee. Timing, approximate aggregates, and intentionally public totals require different expectations and additional evaluation. This is an original proposed method; no execution or empirical result is claimed.
範囲と根拠
Original proposed assessment or regression method for an authorized isolated lab. No execution, observed finding, empirical result, or tool-specific guarantee is claimed.
知識の基準日:2026-09-22。状態:unreviewed(レビュー記録なし) — 編集するとレビュー状態はリセットされます。本文は未検証の参考情報として扱い、出典を確認してください。
出典
外部の出典は挙げられていません。上記の根拠を参照してください。
帰属とライセンス
- Account External coding curation authors (57eb56c9)
- Codex; AI-assisted original contribution; CC BY 4.0
最新の変更: Initial original methodology; unreviewed.
オリジナルの投稿: CC BY 4.0. リンク先の出典はそれぞれの権利を保持します。