Pseudonymising Child Safeguarding Referrals – UK GDPR-compliant anonymisation per DPA 2018 Sch.1 Pt.1

A child safeguarding referral is a statutory record under the Children Act 1989 s.47 (child protection) or s.17 (children in need), identifying the child, parents, siblings, and professionals within special-category health data under DPA 2018 Sch.1 Pt.1. The 8 Caldicott Principles (2013/2020) govern disclosure; DPA 2018 fines reach £17.5 million or 4% of turnover. anonym.legal pseudonymises individuals, preserving the referral narrative.

When this applies

This task applies when child safeguarding referrals and associated records are reviewed by Local Authority audit teams, Child Safeguarding Practice Review panels, or training developers producing case-study materials, and those parties require the safeguarding substance but not the identity of the child or family members. Breaches of child safeguarding confidentiality can attract fines of up to £17.5 million or 4% of annual global turnover under DPA 2018 s.157.

  1. Upload the child safeguarding referral and any associated health visitor, school, or GP records to anonym.legal.
  2. The engine identifies the child's name, date of birth, address, siblings, parents or carers, and the referring professional.
  3. Each named individual is pseudonymised with a consistent pseudonym; family relationships and professional roles are preserved.
  4. The referral narrative, risk indicators, previous safeguarding history references, and multi-agency meeting outcomes are preserved in clear text.
  5. A mapping table is produced with UK data residency.
  6. The pseudonymised referral is released for the approved review or audit purpose.

What you provide

  • Child safeguarding referral form and supporting correspondence
  • Associated health visitor, school, or GP records submitted with the referral

Limitations & cautions

  • Child safeguarding records are subject to additional sensitivity; access controls on the pseudonymised version should be at least as restrictive as those on the original.
  • The tool does not assess the adequacy of the safeguarding response or the risk classification — obtain specialist safeguarding practitioner review.
  • Referrals involving child sexual exploitation or trafficking may contain highly specific details that remain re-identifying even after pseudonymisation — apply additional review. The NHS Records Management Code of Practice 2023 requires child health records to be retained until the child's 25th birthday, or 26th if the record was created after age 17.

FAQ

Can pseudonymised child safeguarding referrals be used in Child Safeguarding Practice Reviews?

Child Safeguarding Practice Reviews under the Children Act 1989 framework typically require identified records for the review panel. Pseudonymised records may be used for the published learning report or for training derived from the review, subject to the panel's data-sharing agreement. According to the ICO's 2021–22 guidance, pseudonymised records remain personal data under UK GDPR and must be governed accordingly.

Are siblings and other children in the household pseudonymised separately?

Yes. Each named child in the household receives a distinct pseudonym; the sibling relationship is preserved without linking to any real child's identity. Children Act 1989 s.17 (children in need) and s.47 (child protection) referrals are treated with the same pseudonymisation rigour, with the statutory basis noted in the output.

Does the tool handle referrals involving multiple agencies — health, school, and police?

Yes. Multi-agency referral documentation is processed in a single batch. Individuals named by different agencies receive consistent pseudonyms across all source documents. The 8 Caldicott Principles require that data shared between health, education, and law-enforcement agencies is minimised and documented.

Healthcare 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 company HQ is in Saarbrücken, Germany. Our servers run in Hetzner's Falkenstein datacenter.

Hetzner holds 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.