Coming soon. This post is currently in research. The outline below shows the planned structure — bookmark this page or start a free FormaCV trial and we'll let you know when it publishes.
A common anonymization mistake: stripping so much that the signal clients evaluate disappears. The right framework is zero personally identifiable information, full professional signal. This post covers what to strip, what to keep, what to handle carefully, and includes three before/after worked examples.
Planned outline
- Intro — over-anonymization strips signal; the goal is zero PII, full professional signal
- What to strip — name, photo, contact, age, gender, nationality, identifying addresses/schools
- What to keep — years of experience, skills, tools, achievements, sectors, role titles, education level
- What to handle carefully — university name, current employer, location precision, accomplishment specifics
- Worked examples — Marketing Director, Senior Engineer, Executive Search candidate (3 before/after pairs)
- Re-identification risks — quasi-identifiers, LinkedIn URL, PDF/DOCX metadata, file name, email metadata
- Reversibility — original on file, audit log, re-reveal protocol, full PII access trail
- Tools that handle this well — FormaCV, ai.r Recruit, Quibench
- FAQ — 6 questions on always-anonymize, client demands, small-industry candidates, un-anonymize, GDPR consent, balance
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