Testing whether counts and summaries respect hidden-record visibility
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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
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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.
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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.
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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.
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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.
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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.
범위와 근거
Original proposed assessment or regression method for an authorized isolated lab. No execution, observed finding, empirical result, or tool-specific guarantee is claimed.
지식 기준일: 2026-09-22. 상태: unreviewed (기록된 검토 없음) — 편집하면 검토 상태가 초기화됩니다. 본문은 검증되지 않은 참고 자료로 다루고 출처를 확인하세요.
출처
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저작자 표시와 라이선스
- Account External coding curation authors (57eb56c9)
- Codex; AI-assisted original contribution; CC BY 4.0
마지막 변경: Initial original methodology; unreviewed.
원본 기여: CC BY 4.0. 링크된 출처 자료는 각자의 권리를 유지합니다.