Anonymising Court of Appeal Criminal Division Papers – UK GDPR-compliant anonymisation per Criminal Procedure Rules

Court of Appeal Criminal Division papers — grounds of appeal, sentencing remarks, and single-judge directions — identify appellants, co-defendants, and victims across detailed legal documents that may be decades old. anonym.legal pseudonymises natural-person identifiers in these papers, enabling case review bodies, researchers, and training providers to engage with appellate reasoning without processing the personal data of named individuals.

When this applies

This task applies when Court of Appeal Criminal Division papers are used for case review, academic research, or advocacy training, and the users require access to the legal reasoning and sentencing analysis but have no legitimate need to process the named individuals' personal data.

  1. Upload the Court of Appeal papers — grounds of appeal, judgment, and supporting exhibits — as a batch.
  2. The engine identifies appellants, respondents, co-defendants, victims, and any named witnesses referenced in the grounds or judgment.
  3. Each individual is pseudonymised consistently across all documents.
  4. The legal reasoning, grounds of appeal, sentencing remarks, and procedural history are preserved in clear text.
  5. A reversible mapping table is produced with UK data residency.
  6. The pseudonymised papers are released for research or training use.

What you provide

  • Grounds of appeal and supporting documentation
  • Court of Appeal judgment (if available)
  • Single-judge directions or full-court listing documents

Limitations & cautions

  • Court of Appeal judgments may be publicly available in reported form — pseudonymisation is appropriate for unreported or internally circulated versions where personal data has not been subject to court-ordered anonymisation.
  • Re-publication of appellate papers in any form may be subject to reporting restrictions — confirm with specialist media or criminal-law counsel before any external publication.

FAQ

Are Court of Appeal judgments already anonymised if published?

Reported Court of Appeal judgments sometimes anonymise parties (particularly in cases involving children or sexual offences), but many do not. For unreported or internally circulated papers, anonym.legal's pseudonymisation provides data-minimisation for review and training purposes.

Can I use pseudonymised appellate papers for criminal-law moot exercises?

Yes. This is a primary use case. The pseudonymised papers preserve the legal substance of the appeal — grounds, reasoning, and outcome — for use in moot or training exercises without exposing the identities of real appellants or victims.

How does the engine handle victim anonymisation orders referenced in the papers?

The engine preserves references to existing anonymisation orders in their original form. It additionally pseudonymises any victim identifiers that appear in the papers, providing a consistent data-minimisation layer on top of any court-ordered protections.

Criminal Records

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.