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Open Source Static Scanning tool to detect data flows in your code, find data security vulnerabilities & generate accurate Play Store Data Safety Report.
A curated list of resources related to privacy engineering
A privacy and security engineering toolkit: Discover, understand, pseudonymize, anonymize, encrypt and securely share sensitive and personal data: Privacy and security as code.
Open-source description language for privacy to declare data types and data behaviors in your tech stack in order to simplify data privacy globally. Supports GDPR, CCPA, LGPD and ISO 19944.
Python SDK for PII detection and redaction in text and images, combining regex + NLP pipelines for production privacy workflows.
282+ structured privacy & data protection skills for AI agents. GDPR, CCPA, EU AI Act, HIPAA, LGPD, PIPL, DPDP Act.
This repository hosts a persona based privacy threat modeling solution called Models of Applied Privacy or MAP.
Overview of tools to de-identify, synthesize and work safely with (sensitive) data
🌹 Thorn is an open-source, data privacy vault to store and manage PII in a fully compliant manner.
Mysto privacy engineering toolkit for de-identifying data
Free Play Store Data Safety Generator. Privado CLI scans & monitors your repositories to build up privacy-transparency reports & finds privacy issues.
Privacy as Code for your CI and runtime environment. This repository is a Quickstart to show how Fides can be used in a CI/CD pipeline.
generated portfolio by coursed in privacy engineering udacity
Per-Query Role-Based Data Minimization in GraphQL
The Peter Horvath Improvement Proposal repository
Privacy and log encryption. Enterprise-grade encrypted log processing system with Zero Trust architecture. AES-256-GCM encryption, PII redaction, tamper-evident audit trails. Python/FastAPI/Kubernetes. GDPR/HIPAA compliant.
Per-Query Role-Based Data Minimization in GraphQL
Open-source Codex skill package for CNIL-based GDPR privacy engineering guardrails.
Mediscan-IQ is a new developed tool to de-identify clinical text, summarize findings, and tag case risk (low / moderate / high) — via CLI, REST API, or Streamlit.
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