Anonymize EEO-1 report source data for internal auditing – CCPA/HIPAA-compliant de-identification per 29 CFR §1602

EEO-1 reports aggregate workforce demographics by race, ethnicity, sex, and job category under EEOC record-keeping rules at 29 CFR §1602. The underlying source data links demographic attributes to individual employees. anonym.legal pseudonymizes those linkages so HR analytics teams can audit pay-equity and workforce-composition trends without exposing individual identity-and-demographic pairings.

When this applies

Apply this workflow before sharing EEO-1 source datasets with external diversity consultants, internal analytics teams, or litigation-support vendors where the task is workforce-composition analysis rather than individual-employee review.

  1. Upload the EEO-1 source file (CSV, XLSX, or structured export) to anonym.legal.
  2. The engine maps each row's employee identifier fields — name, employee ID, SSN fragment — against the EEOC's 29 CFR §1602 data-element taxonomy.
  3. Each employee record is assigned a consistent pseudonymous identifier that persists across the dataset, preserving the statistical linkage between job category and demographic data.
  4. Direct identifiers (names, partial SSNs, home addresses) are replaced while protected-class fields (race, ethnicity, sex, job category code) are retained for analysis.
  5. The output dataset is exported in the same structured format as the source file for seamless import into analytics tools.
  6. A reversible mapping key is stored encrypted for re-identification if individual follow-up is required.

What you provide

  • EEO-1 source data file in CSV, XLSX, or structured HR-system export format
  • Field mapping confirming which columns represent direct identifiers vs. demographic attributes
  • Scope definition: single establishment or multi-establishment consolidated report

Limitations & cautions

  • anonym.legal does not prepare or submit the EEO-1 report itself; the tool processes source data only.
  • Demographic fields are retained, not anonymized, because removing them defeats the purpose of equity analysis; re-identification risk from demographic combinations should be assessed separately.
  • State EEO reporting requirements may differ from federal EEO-1 requirements and are not addressed by this workflow.
  • Small demographic cells (fewer than 5 individuals per category) may still carry re-identification risk despite pseudonymization; statistical disclosure-limitation techniques should be applied separately.

FAQ

Does this workflow cover both Type 1 and Type 2 EEO-1 consolidated reports?

Yes. The tool processes the underlying source dataset regardless of whether you are preparing a single-establishment or multi-establishment consolidated report. The pseudonymization applies at the individual-employee row level across all establishment units.

Will pseudonymizing the source data affect the accuracy of the final EEO-1 submission?

No. The pseudonymized dataset is for internal analysis only. The final EEO-1 submission is prepared from your authoritative HR system; you re-identify the records using the stored mapping key before any submission to the EEOC.

Can this workflow help with pay-equity analysis beyond the EEO-1 report?

Yes. The pseudonymized dataset can be enriched with additional compensation fields before sharing with consultants, enabling pay-equity regression analysis without disclosing individual employee identities.

Employment Law

About this page

We update this page when our platform or the law changes.

Read our founder note for how we work.

Each change shows up in the timestamp at the top.

We follow these rules

  • GDPR (EU 2016/679).
  • ISO/IEC 27001:2022.
  • NIS2 (EU 2022/2555).
  • HIPAA safe harbor under 45 CFR § 164.514(b)(2).

Our promise

We do not sell your data.

We do not train models on your text.

We store your files in Germany.

You can delete your account at any time.

You own your work.

Where we run

Our servers live in Falkenstein, Germany.

We use Hetzner. They hold ISO 27001 certification.

All data stays in the EU.

Backups run every day.

Need help?

Email support@anonym.legal.

We reply within one business day.

How we test

We run a full check suite on every release.

Each surface gets its own sweep script and report.

Human reviewers spot-check the output each week.

We track recall and precision on a labelled set.

Bad runs block the deploy.

What we never do

  • We never sell your information to third parties.
  • We never train models on what you upload.
  • We never keep your work after you delete it.
  • We never share keys with any outside firm.
  • We never run ads inside the product.

Plans in plain words

We sell credits, not seats.

One credit covers one short job.

Long jobs use a few credits each.

You can top up at any time.

Unused credits roll over each month.

Read the plans page for current rates.

Who built this

A small team of engineers and lawyers built this.

We ship from Europe and work in the open.

Our founder note spells out why we started.

Where to start

How the parts fit

A browser add-on cleans text inside Chrome.

A Word plug-in handles drafts in Office.

A small desktop tool works on whole folders.

An agent protocol link feeds large models safely.

All four share one core engine and one rule set.

Words from our team

We started this work after a lunch about cookies.

One friend kept getting odd ads on her phone.

We asked why a court file leaked through a draft.

We sketched the first build on a napkin that week.

By month three we had a tiny demo for a friend.

She used it on her first case the next day.

Common questions we hear

Can the tool read scanned PDFs? Yes, with OCR.

Does it work on long files? Yes, in small chunks.

Can I roll my own rule set? Yes, save it as a preset.

Does it run offline? The desktop build runs offline.

Do you keep my files? No, the cloud build wipes after each run.

Will it learn from my work? No, we never train on inputs.

A short tour of the workflow

Upload a file or paste a snippet of prose.

Pick the entities you want gone from the draft.

Choose a method: replace, mask, hash, encrypt, or redact.

Press run and watch the side panel show each hit.

Skim the result and tweak any rule that misfired.

Save the cleaned file or send it to a teammate.