By George Curta · Last updated 2026-06-30
Research Data Sharing Without Privacy Risks
Share participant data safely across institutions and publications. anonym.legal's reversible encryption enables longitudinal re-identification when ethically approved, while maintaining HIPAA Safe Harbor compliance for de-identified datasets.
課題
研究機関はデータ共有とプライバシーの間でジレンマを抱えています：
- •研究倫理は参加者のプライバシー保護を要求しています
- •共同研究は機関をまたいだデータ共有が必要です
- •縦断的研究は一貫した仮名が必要です
- •論文発表に識別可能な情報を含めることはできません
ソリューション
研究データのための一貫性のある再現可能な仮名化。
ソリューション
一貫した ID
ドキュメントをまたいで同じ識別子には同じ仮名を使用。縦断的研究に最適です。
再現可能
同じデータを再処理して同一の結果を得られます。
安全な共有
参加者のプライバシーを損なうことなく共同研究者とデータセットを共有します。
研究フォーマット
一般的な研究フォーマットの CSV、JSON、構造化データをサポート。
Research Applications
From clinical trials to social science surveys, anonym.legal supports the full research data lifecycle.
Data Sharing & Publication
- De-identify datasets for open science repositories
- Anonymize quotes and excerpts in publications
- Safe cross-institution collaboration
Longitudinal Studies
- Reversible encryption for approved re-identification
- Consistent hashing to link records across time points
- Full audit trails for IRB documentation
Reversible for Approved Re-identification
Unlike permanent redaction, anonym.legal's reversible encryption lets you decrypt anonymized data when your IRB approves re-identification—essential for longitudinal research, follow-up studies, and data linking.
- Follow-up Contacts: Re-identify participants for study continuation
- Data Linking: Match anonymized records across datasets
- IRB Documentation: Full audit trail for ethics compliance
Research Workflow
Collect data with informed consent
Store encryption key securely
Anonymize for analysis
Work with de-identified data
Publish de-identified results
Safe data sharing
Re-identify when IRB approves
Follow-up studies, data linking
Trusted by researchers
Limitations / Important note
- De-identification reduces but does not eliminate re-identification risk; assess residual risk for your dataset and release context.
- Detection is probabilistic; high-stakes outputs should be human-reviewed. This is not legal advice.
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 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
- Open the web app and try a sample file.
- Learn how credits get counted.
- See current plans and limits.
- Meet the team behind the product.
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.