Anonymising Child Arrangements Order Applications – UK GDPR-compliant anonymisation per Children Act 1989

A C100 application for a Child Arrangements Order under the Children Act 1989 identifies the applicant, respondent, the child, and often extended family members by name and address. anonym.legal pseudonymises those identifiers while preserving proposed arrangements, welfare concerns, and contact schedules so mediators and counsel can assess the application without unnecessary personal-data exposure.

When this applies

This task applies when a C100 application and position statements are shared with a mediator assessing MIAM compliance, a jointly-instructed psychologist, or a reviewing solicitor, and those recipients require the welfare and contact narrative but not the parties' personal identities.

  1. Upload the C100 form and any accompanying position statements or supporting documents.
  2. The engine identifies the child, applicant, respondent, and any named third-party family members across all documents.
  3. Each individual receives a unique, consistent pseudonym; proposed contact arrangements, holiday schedules, and welfare concerns are preserved in clear text.
  4. School names and GP details embedded in the application are pseudonymised where they identify specific individuals.
  5. A reversible mapping table is produced with UK data residency.
  6. Release the pseudonymised documents to the mediator or expert; restore real identities before court filing.

What you provide

  • C100 application form
  • Position statements from both parties
  • Any MIAM exemption evidence or mediator's certification

Limitations & cautions

  • The court copy of the C100 must bear the parties' real names; the pseudonymised version is for pre-proceedings use only.
  • Where the application involves allegations of domestic abuse, the non-molestation order workflow may be more appropriate to manage sensitive address information.
  • anonym.legal does not assess the merits of the proposed child arrangements or the welfare checklist under the Children Act 1989.

FAQ

Can I use the pseudonymised C100 in MIAM mediation sessions?

Yes, subject to your mediator confirming that a pseudonymised version satisfies their procedural requirements. Many mediators work from position statements rather than the court form itself.

Are school names treated as personal data in the application?

School names alone are not personal data, but a named school combined with a named child and address can amount to a combination that identifies the child. The engine treats such combinations as indirect identifiers and pseudonymises them.

Does the tool handle C100 applications submitted through the Online Family Court?

Yes. PDFs or DOCX exports of applications originally submitted online are processed identically to paper forms.

What if both parties have submitted separate position statements?

Upload both position statements in the same batch. Each party's personal data receives consistent pseudonyms across both documents, preserving the adversarial structure of the positions.

Family 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.