Anonymize collective bargaining documents for negotiations analysis and legal review – CCPA/HIPAA-compliant de-identification per NLRA §158

Collective bargaining proposals and counter-proposals under NLRA §158 duty-to-bargain obligations may reference individual employee wage rates, benefit levels, and disciplinary histories as bargaining data. anonym.legal pseudonymizes those individual references so negotiating teams and labor counsel can analyze bargaining-session records and cost models without unnecessary exposure of employee personal data.

When this applies

Apply this workflow before sharing bargaining session notes, wage-cost models, or benefit-comparison analyses with labor economists, outside negotiating counsel, or management negotiating teams where individual employee identifiers are not required for the analysis.

  1. Upload bargaining session notes, union wage proposals, management counter-proposals, and cost-modeling spreadsheets to anonym.legal.
  2. The engine scans for individual employee identifiers embedded in wage exhibits, seniority lists, and disciplinary history references.
  3. Each named employee is assigned a consistent pseudonym across all bargaining documents.
  4. Aggregate wage rates, job classifications, seniority tiers, and benefit cost summaries are retained as structural content for negotiations analysis.
  5. The pseudonymized documents are exported for negotiating team or labor counsel review.
  6. A reversible mapping key is stored for re-identification of specific employees if individual contract determinations arise during negotiations.

What you provide

  • Bargaining session notes and proposals in PDF or DOCX format
  • Wage and benefit cost-modeling spreadsheets in CSV or XLSX format
  • Seniority lists and disciplinary history summaries referenced in negotiations

Limitations & cautions

  • anonym.legal does not assess whether specific bargaining positions comply with the NLRA §158 duty to bargain in good faith; that legal assessment requires labor counsel.
  • Seniority lists that are contractually required to be provided to the union in identified form cannot be pseudonymized for that purpose; this workflow covers internal analysis copies only.
  • State public-sector collective bargaining statutes may impose different disclosure requirements and are not addressed by this federal NLRA workflow.
  • Wage-cost models with very small employee populations per classification may carry re-identification risk from the aggregate data alone.

FAQ

Can the tool pseudonymize seniority lists while preserving relative seniority rankings?

Yes. The engine replaces employee names with consistent pseudonyms while retaining seniority dates and ranking positions, so the relative order remains intact for analysis purposes.

Will aggregate cost figures in wage proposals be affected by pseudonymization?

No. Aggregate totals and per-classification averages are structural content and are preserved. Only the names or employee IDs that link a specific wage rate to a named individual are pseudonymized.

Is this workflow suitable for preparing economic analysis for interest arbitration?

Yes for internal preparation and counsel review. As with grievance arbitration, confirm with the arbitrator and opposing party that pseudonymized exhibits are acceptable before including them in formal interest-arbitration submissions.

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.