Anonymize creditors matrices for docket research and training – CCPA/HIPAA-compliant de-identification per FRBP Rule 1007

The creditors matrix filed under FRBP Rule 1007 lists the names and mailing addresses of all creditors in a case, enabling court-issued notices. It is essentially a directory of personal and commercial contacts. anonym.legal pseudonymizes names and addresses across the matrix so it can be used for claims-management training, system-testing, or academic creditor-behavior research without disclosing real parties.

When this applies

Apply this workflow when a creditors matrix or mailing list must be provided to a claims agent, noticing vendor, or researcher for testing or training purposes where actual creditor identities are not required.

  1. Upload the creditors matrix in PDF, TXT, or CSV format to anonym.legal.
  2. The engine parses each creditor entry, identifying name lines and address components — street, city, state, ZIP.
  3. Each creditor entity or individual is assigned a consistent pseudonym applied across all occurrences.
  4. Address components are replaced with synthetically generated US postal addresses.
  5. The encrypted mapping is stored for authorized re-identification.
  6. The pseudonymized matrix is exported in the same format as the original for drop-in use with noticing systems.
  7. Bulk processing supports large matrices from mega-cases with thousands of creditor entries.

What you provide

  • Creditors matrix in PDF, TXT, or CSV format
  • Any supplemental or amended matrices filed after the initial submission
  • Indication of whether entity names and individual names should be treated differently

Limitations & cautions

  • The tool does not verify that all required creditors are listed; that obligation rests with the debtor under FRBP Rule 1007.
  • Governmental units on the matrix — such as the IRS or US Trustee — may warrant special handling and should be reviewed manually.
  • Address pseudonymization generates plausible but synthetic postal addresses; they should not be used for actual noticing.
  • Very large matrices from complex Chapter 11 cases may require processing in segmented batches.

FAQ

Can the pseudonymized matrix be loaded directly into a claims-management system for testing?

Yes. The exported matrix maintains the same format and structure as the original, making it suitable for system-integration testing or noticing-vendor platform testing without exposing real creditor data.

How are duplicate creditor entries handled?

The engine detects duplicate entries by name and applies the same pseudonym to all duplicates, preserving the deduplication logic of the original matrix.

Are government creditors like the IRS pseudonymized?

By default, governmental unit names are flagged for manual review rather than automatically pseudonymized, because their names are not personal data and their presence on a matrix is a matter of public record.

What format is the exported matrix?

The pseudonymized matrix is exported in the same format as the uploaded original — PDF, TXT, or CSV — to ensure compatibility with court filing and noticing systems.

Bankruptcy & Insolvency

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.