Pseudonymising SDLT Returns for Tax Compliance Review – UK GDPR-compliant anonymisation per UK GDPR

A Stamp Duty Land Tax (SDLT) return (SDLT1 and supplementary forms) discloses the purchaser's name, address, and National Insurance number or UTR, the vendor's details, and the full transaction consideration. anonym.legal pseudonymises those personal and financial identifiers — preserving the property details, consideration, relief claimed, and tax calculation — so SDLT returns can be reviewed by tax advisers or compliance teams without exposing the parties' full personal tax profiles.

When this applies

This task applies when draft or completed SDLT returns are reviewed by tax counsel, external compliance teams, or internal quality-assurance reviewers who need to assess the tax calculation, relief claims, and return accuracy without processing the parties' personal tax identifiers.

  1. Upload the draft or completed SDLT return (SDLT1 and any supplementary forms SDLT2, SDLT3, SDLT4) to anonym.legal.
  2. The engine identifies the purchaser's name, address, National Insurance number or UTR, and vendor's personal details in the return.
  3. Each natural person and their tax identifiers are pseudonymised consistently; the property description, transaction date, consideration, relief claimed, tax liability, and effective date of transaction are preserved.
  4. Supplementary forms are processed in the same batch with consistent pseudonymisation across the full return.
  5. A mapping table is produced with UK/EU data residency.
  6. Release the pseudonymised return for tax compliance review; restore originals before submission to HMRC.

What you provide

  • SDLT1 return form (draft or completed)
  • SDLT2, SDLT3, SDLT4 supplementary forms (if applicable)
  • Any accompanying SDLT relief claim supporting documentation

Limitations & cautions

  • SDLT returns must be submitted to HMRC in the purchaser's real name within the statutory deadline — the pseudonymised version is for internal tax review only.
  • National Insurance numbers and UTRs are sensitive tax identifiers; ensure the mapping table is securely retained and access is restricted to authorised personnel.
  • The tool pseudonymises personal data but does not assess the correctness of the SDLT calculation or relief claims — obtain specialist property tax advice.

FAQ

Are National Insurance numbers pseudonymised in the SDLT return?

Yes. National Insurance numbers and UTRs are personal data under UK GDPR and are pseudonymised. They are replaced with consistent pseudonym identifiers in the review copy.

Is the SDLT consideration figure preserved?

Yes. The consideration amount, the effective date of transaction, and the SDLT tax liability are all preserved in clear text. These are transaction details rather than personal data.

Can I use this for a portfolio transaction involving multiple SDLT returns?

Yes. Upload all SDLT returns in a single batch. Purchasers appearing across multiple returns receive consistent pseudonyms.

Property & Conveyancing

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.