de-identification

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.

Offline PII firewall for AI agents and LLM apps: fast local detection and redaction, Claude Code hook, LiteLLM guardrail. Zero network calls, one dependency.

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

Deidentify people's names and gender specific pronouns

DICOM gateway for de-identification or tag morphing

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

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

Masking identifiable information from health related documents.

Local-first PII anonymization and de-anonymization in Go. Multilingual via GLiNER NER + 73 pattern recognizers. Self-host runs as a single binary that never leaves your network.

PII Anonymizer service based on python with FastAPI

READI

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.

教育隱私保護系統 — 純前端執行的去識別化與還原工具。Zero-Trust Data Anonymization Shield for AI.

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.

Deterministic-first PII/PHI de-identification you can prove: benchmarked recall, jurisdiction policy packs, reversible tokenization, audit trail. CLI + library + GitHub Action; optional GLiNER / Gemma contextual tier.

A data de-identification library written in Go