An open-source framework for detecting, redacting, masking, and anonymizing sensitive data (PII) across text, images, and structured data. Supports NLP, pattern matching, and customizable pipelines.

ARX is a comprehensive open source data anonymization tool aiming to provide scalability and usability. It supports various anonymization techniques, methods for analyzing data quality and re-identification risks and it supports well-known privacy models, such as k-anonymity, l-diversity, t-closeness and differential privacy.

Mediapipe-based library to redact faces from videos and images

A curated list of resources related to privacy engineering

Examples scripts that showcase how to use Private AI Text to de-identify, redact, hash, tokenize, mask and synthesize PII in text.

Baseline Recipe for VoicePrivacy Challenge 2022: anonymization systems and evaluation software

Deidentify people's names and gender specific pronouns

A python client used to interact with the Private AI's API

Masking identifiable information from health related documents.

A pipeline to identify (and remove) certain sequences from raw genomic data. Default taxon to identify (and remove) is Homo sapiens. Removal is optional.

CliniDeID automatically de-identifies clinical text notes according to the HIPAA Safe Harbor method. It accurately finds identifiers and tags or replaces them with realistic surrogates for better anonymity.

Application of our De-identification Framework with open source technologies, enabling enterprises to take ownership of the de-identification process and deploy it in trusted environments.

A data de-identification library written in Go

A pre-commit hook to check for PII in your code.

Named entity recognition framework

Source code for the paper "Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records" in Future Internet (2021).

가명처리 라이브러리

Python package to replace identifiable strings in multiple files and folders at once.

An named-entity-recognition (NER) based anonymizer for archival documents metadata.

This is the easiest way to de-identify license plates.