Ai4Privacy.Find personal information in text and replace it with consistent placeholders.
On 12 March 2024, John Smith
visited 123 Wall St, New York
and paid $1,250.00 on his
company card.
On 12 March 2024, John Smith
visited 123 Wall St, New York
and paid $1,250.00 on his
company card.
On [DATE_1], [GIVENNAME_1]
[SURNAME_1] visited [STREET_1],
[CITY_1] and paid [AMOUNT_1]
on his company card.
The records are synthetic, the labels are visible and each dataset release states its own coverage, access and license.
On 12 March 2024, Elena Rossi booked a flight from Milan Malpensa Airport to Paris Charles de Gaulle for 2 guests, departing at 07:15.
Choose the path that matches where your text lives and what you need to do with it.
Use curated synthetic PII data for training, benchmarking and evaluation.
Mask sensitive information in the browser before prompts are sent.
Connect hosted detection and masking to an application.
Run the pipeline inside your own Python or JavaScript environment.
Coverage, labels and evaluation slices are visible before you choose a release.
PII Masking 3M spans 30 languages across three regions. Its collection includes 20 core entity types and 61 industry-specific types, with pre-computed mBERT-compatible BIO labels.
Mask a prompt in the browser, connect a hosted API or run detection inside your own application.
Ai4Privacy Chat masks personal information in the browser before prompts are sent.
Open Ai4Privacy ChatUse the hosted REST API when you want your application to send text and receive detected or masked text back.
Request API accessInstall the Python or JavaScript package and run the pipeline inside your own application.
$ pip install ai4privacy$ npm i @ai4privacy/ai4privacyDefine the policy, evaluate detection and apply masking decisions consistently.
A practical method for moving from entity policy to evaluated protection.
Set the entity taxonomy and annotation policy for your context.
Evaluate spans, confidence and errors against labelled examples.
Apply consistent replacements while retaining useful structure.
p5y is Ai4Privacy’s open framework for describing personal and sensitive data, inspired by i18n and l10n.
The training data names the entity type.
The detector returns the same category.
The replacement keeps a stable, readable label.
Errors can be reviewed by entity type.
Short answers about data, deployment and licensing.
Still deciding? Tell us what you are building
Ai4Privacy helps teams find personal information in text and replace it with placeholders. The workflow is available through synthetic datasets, a browser-based chat, a REST API and local Python and JavaScript SDKs.
The datasets presented here use synthetic PII rather than real personal data. Check the individual release page for its coverage, access and license details.
Yes. The Python and JavaScript packages are designed for local execution inside your application or workflow. The REST API is the separate hosted integration path.
Start from the region and languages in your text. The Asia-Pacific and European releases focus on those regions, the 1M series is the open-core option, and the 400K series and below support comparisons at smaller release sizes.
Each release states its own license. The 1M series is CC-BY-4.0 open core. The 3M and 2M releases are listed as CC-BY-4.0 plus Enterprise. The 400K series and below use the Ai4Privacy Dataset and Derivative Products License.
Choose the local packages when detection needs to run in your own environment. Choose the hosted REST API when your application should send text and receive detected or masked text back.
The datasets are on Hugging Face, the projects are on GitHub, and the packages install from PyPI and npm.