NVIDIA-NeMo / NVIDIA-NeMo/Anonymizer
feat(rewrite): revisit HIGH sensitivity classification and any_high_leaked trigger for needs_human_review
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- Dominant language
- Python
- Stars
- 122
- Forks
- 17
- Avg merge
- 4d 2h
- Merged PRs (30d)
- 11
Description
Background
needs_human_review is triggered by two conditions: leakage_mass > flag_leakage_mass_above (default 2.0) or any_high_leaked=True. Investigation of TAB runs showed all flagged rows were triggered by any_high_leaked, not leakage mass.
The entities flagged as HIGH sensitivity were:
court_name(e.g. 'District Court', 'Budapest District Court') — fixed by PR #94 domain supplementslegal_role(e.g. 'defendant_in_recovery_action')former_senator(occupation type)
These are quasi-identifiers at most. Flagging them as HIGH causes needs_human_review=True even when leakage mass is low and utility is good (e.g. utility=0.97, leakage=0.96).
Questions to resolve
- What is the right criteria for HIGH vs. MEDIUM sensitivity? Should quasi-identifrs ever be HIGH?
- Should
any_high_leakedalone be sufficient to triggerneeds_human_review, or should it require a minimum leakage mass too? - Should sensitivity level be configurable per domain (e.g. LEGAL may have different thresholds than MEDICAL)?
Related
- PR #94 partially addressed this for
court_namevia LEGAL domain supplements - Ties into the domain-specific risk tolerance work removed before release (see domain supplement follow-up issue)
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Review the implementation of sensitivity classification and the needs_human_review trigger, then compare it with PR #94 and the domain supplement follow-up issue. Resolve the HIGH versus MEDIUM criteria, the role of any_high_leaked, and whether domain-specific thresholds are needed. Done means the policy is agreed and reflected in the relevant behavior and validation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- security
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100