Anonymising Restorative-Justice Case Files – UK GDPR-compliant anonymisation per UK GDPR Art. 10

Restorative-justice case files record the offender's criminal background, victim impact statements, conference preparation notes, and outcome agreements — combining criminal-conviction data under UK GDPR Art. 10 with sensitive personal narratives from both offender and victim. anonym.legal pseudonymises all personal identifiers, enabling restorative-justice practitioners and training bodies to review case quality without retaining unnecessary personal data.

When this applies

This task applies when restorative-justice case files or conference records are reviewed by quality-assurance assessors, training supervisors, or researchers studying restorative-justice outcomes, and those reviewers require the case content but not the personal identifiers of the offender, victim, or support persons.

  1. Upload the restorative-justice case file — including referral documents, victim impact statement, preparation notes, and outcome agreement.
  2. The engine identifies all named individuals: offender, victim(s), support persons, and named facilitators.
  3. Each individual is assigned a distinct, consistent pseudonym applied throughout the case file.
  4. Offence summary, impact description, outcome-agreement terms, and compliance monitoring records are preserved.
  5. A reversible mapping table is produced with UK data residency.
  6. The pseudonymised file is released for quality-assurance or research review.

What you provide

  • Restorative-justice referral document
  • Victim impact statement or preparation notes
  • Conference record and outcome agreement

Limitations & cautions

  • Victim impact statements carry special sensitivity — even pseudonymised versions should be shared only within a strictly limited quality-assurance or training cohort with appropriate data-processing agreements.
  • Outcome agreements may impose obligations on named individuals; pseudonymised versions must not be used as the basis for enforcement or compliance monitoring.

FAQ

Are support persons — such as a family member accompanying the victim — also pseudonymised?

Yes. All named natural persons appearing in the case file — including support persons for both offender and victim — are detected and pseudonymised with distinct pseudonyms.

Can pseudonymised restorative-justice files be used in facilitator training programmes?

Yes. This is a primary use case. The pseudonymised file preserves the case dynamics, outcome-agreement terms, and facilitation challenges needed for training without exposing the identities of real offenders or victims.

How does pseudonymisation interact with victim-consent requirements in restorative justice?

Victim consent governs participation in the restorative-justice process itself, not the subsequent quality-assurance or training use of pseudonymised records. Ensure your organisation's data-sharing policy covers training use of pseudonymised case files.

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.