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Reversible De-ID for Clinical Research

When a study finds unexpected biomarker risk in 47 of 5,000 participants, researchers need to contact real patients. Only 23% of anonymization tools offer.

April 21, 20269 minute read
reversible de-identificationclinical research pseudonymizationpatient re-contact protocolIRB data managementHIPAA reversible encryption

Reversible De-ID for Clinical Research

Long trials face a hard tradeoff. Patients must stay hidden during the study. IRB rules require it. Patient trust depends on it. But a result may require re-contact later. Permanent de-ID removes that path. Reversible de-ID keeps it open.

See how we support this in our compliance overview and security practices.

The Re-Contact Problem

An oncology center runs a 5,000-patient study. Mid-trial, 47 patients show markers tied to an aggressive cancer type. This was not in the original scope. The ethics board reviews the finding. It approves re-contact. Duty to warn applies.

If the original de-ID was permanent, the team is stuck. Random codes with no map give no path back. The 47 records cannot link to real patients. The finding cannot be acted on. Patients who may need care cannot be reached. The privacy setup has failed at its most critical point.

This is not rare. Any long trial can hit an unexpected finding. Duty-to-warn doctrine requires action when risk is found. Without a re-ID path, that action is not possible.

GDPR Key Separation Rules

EDPB Guidelines 05/2022 address this problem directly. Pseudonymization is a valid data protection step. It keeps the option to re-identify open. An approved process can use it when needed.

The core rule is key separation. The decryption key must be kept apart from the pseudonymized data. Controls must block any access that is not approved. The team using the data must not also hold the key. Re-ID must require a formal, logged step.

IAPP's 2024 survey found that only 23% of anonymization tools offer true reversibility. Most apply permanent masking or replacement. Those methods block the re-contact that duty-to-warn requires.

How the Architecture Works

A compliant setup uses reversible encryption with AES-256-GCM. Each patient ID is turned into a token. The same patient maps to the same token across all study files. Data links stay intact. No raw IDs appear in the working set.

The decryption key is held by a data custodian. It is kept apart from the data. Any use of the key requires a written, approved request.

The team works only with tokens during analysis. When the 47 affected patients are flagged, the ethics board approves re-ID. The custodian applies the key to those 47 records only. The team gets real IDs for those 47. The other 4,953 patients stay protected.

Only targeted re-ID is possible. The rest of the dataset is never touched.

For more on how pseudonymization differs from full anonymization, see our GDPR anonymization vs pseudonymization guide.

Sources

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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.

Related reading

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.