Detect and mask PII across your AI workflow

Find personal information in your text and replace it with placeholders. Ai4Privacy gives you the synthetic datasets, the browser-based chat, the REST API and the local SDKs to do it.

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Original synthetic record · exposed fields underlined

The set design, inspired by a miniature garden at Rectory Road 653 in Hungerford Kintbury, reflects the intimate scale of bonsai art.

language: en
region: GB
script: Latn
split: train
uid: 20986105

One verified synthetic record from Ai4Privacy’s OpenPII 1M dataset. The interaction changes the view; it does not generate or modify the record.

[data-dataset-specimen] [role='tablist'] { display: none; } [data-dataset-specimen] .dataset-specimen__stage { display: block !important; } [data-dataset-specimen] [role='tabpanel'][hidden] { display: block !important; visibility: visible !important; }

Cited by

Ai4Privacy’s work is cited in research and industry publications from these institutions. Each logo links to its source.

Four ways to use Ai4Privacy

Training a model, working with prompts, shipping a product or running it locally. Compare all products, then choose the path that matches the work in front of you.

Choose a dataset release

Use synthetic, multilingual records for training and evaluation without introducing real personal data. Compare releases by scale, coverage, license and best fit.

PII Masking 3MAsia-Pacific Release

Scale: 3M+ rows
Coverage: 30 languages · 3 regions
License: CC-BY-4.0 + Enterprise
Best fit: Asia-Pacific training and evaluation

PII Masking 2MEuropean Release

Scale: 2M+ rows
Coverage: 32 locales · 98 entities
License: CC-BY-4.0 + Enterprise
Best fit: European training and evaluation

PII Masking 1MSeries

Scale: 1M series
Coverage: 11 regions
License: CC-BY-4.0 open core
Best fit: Open-core model training

PII Masking 400Kand Below

Scale: 400K+ largest release
Coverage: 6 languages · 17–54 entities across the series
License: Ai4Privacy Dataset and Derivative Products License
Best fit: Comparing smaller release sizes

Browse all datasets

Use PII masking directly

Use Chat, the REST API or local SDKs when you need masking capability directly rather than training and evaluation data.

Mask sensitive text before it reaches AI chat

Ai4Privacy Chat masks personal information in the browser before prompts are sent.

Ai4Privacy ChatSynthetic example

You

Draft a reply to [NAME_1] about order [ORDERNUMBER_1].

Of course. What would you like the reply to say?

2 fields masked locally

Open Ai4Privacy Chat

Add PII detection and masking to your product

Call the REST API for detection, redaction, synthetic identities and privacy-aware chat.

InputMaya Chen

HostedREST API

Output[NAME_1]

Request API access

Run PII detection locally

Install the Python or JavaScript package and run detection inside your own application.

Terminal

$ pip install ai4privacy$ npm i @ai4privacy/ai4privacy

PythonJavaScriptRuns locally

View the Python package View the JavaScript package

Build a masking pipeline you can explain

Learn how to define entity types, prepare training data, evaluate models and apply masking decisions consistently.

PII Masking for AI Training Data: A Practitioner’s Guide

PII masking for AI training data is the process of finding personal data in a text corpus and replacing it, so that less identifiable information is carried into a model’s weights.

Read the practitioner’s guide

DefineInventoryChoose the personal-data types that matter.

FindDetectLocate each personal span in context.

ProtectReplaceKeep useful language around the placeholder.

Explore the complete guide set

PII Annotation Guidelines: Labeling Personal Data for NER Training

Read the annotation guide

False negative

Email maya@example.org today.

PII left exposed

False positive

Ship the [DATE_1] release.

Useful context removed

A shared language for personal data

p5y is Ai4Privacy’s open framework for describing personal and sensitive data. It is inspired by i18n and l10n.

English (en-US)

Ms. North, our office in Essex confirmed your visit with a health psychologist at 10:02 AM.

p5y redacted English (en-US-P)

[TITLE_1] [NAME_1], our office in [COUNTY_1] confirmed your visit with [MEDICAL_PROFESSIONAL_1] at [TIME_1].

Privacy masking treated as a translation task. Adapted from the p5y framework diagram.

01
AwarenessDescribe what personal data is present.
02
ProtectionApply a shared protection vocabulary.
03
Quality assuranceCheck the result consistently.

Read the p5y specification

Adoption in practice

Research citations show relevance. These figures show how the work has been distributed and expanded by Artificial Intelligence Suisse SA and its partners.

3M+ · 30 · 3Examples, languages and regions

Ai4Privacy and VNCyberS expanded the Asia-Pacific release into a multilingual corpus spanning three regions.

Read the partner release 15M+downloads on Hugging FaceOpen the Ai4Privacy organization

Questions before you choose a path

Short answers to the practical questions teams ask when comparing datasets, local packages and hosted access.

What does Ai4Privacy do?

Ai4Privacy helps teams find personal information in text and replace it with placeholders. The same workflow is available through synthetic datasets, a browser-based chat, a REST API and local Python and JavaScript SDKs.

Do the training datasets contain real personal data?

The datasets presented here use synthetic PII rather than real personal data. Each release has its own coverage, access and license details, so those facts should be checked on the individual release page.

Can PII detection run inside my own application?

Yes. The Python and JavaScript packages are designed for local execution inside your own application or workflow. The REST API is the separate hosted integration path.

Start with our open-source work

The datasets are on Hugging Face, the projects are on GitHub, and the packages install from PyPI and npm.

If you are evaluating Ai4Privacy for your company, talk to us about access and integration.

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