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.

Countly is a privacy-first, AI-powered analytics and engagement platform for understanding and optimizing customer journeys across digital applications, from desktop and mobile to IoT and connected environments.

Awesome Machine Unlearning (A Survey of Machine Unlearning)

Diffprivlib: The IBM Differential Privacy Library

OpenHuFu is an open-sourced data federation system to support collaborative queries over multi databases with security guarantee.

All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.

Privacy Meter: An open-source library to audit data privacy in statistical and machine learning algorithms.

The Privacy Engineering & Compliance Framework

Privacy details of SDKs for Apple Privacy Nutrition & Google Safety Section disclosure.

Filter sensitive information from free text before sending it to external services or APIs, such as chatbots and LLMs.

An easy-to-use federated learning platform

The modern spam protection. Protects your forms from spam by simply checking the content. Open source, Free to use, Accessible, and Self-Hosted.

VeritasGraph: Enterprise-Grade Graph RAG for Secure, On-Premise AI with Verifiable Attribution

A permissionless blockchain network to manage digital identity and access rights

Data security framework for Clojure

[ICLR24 (Spotlight)] "SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation" by Chongyu Fan*, Jiancheng Liu*, Yihua Zhang, Eric Wong, Dennis Wei, Sijia Liu

Claude Skills for Governance, Risk & Compliance (GRC): Expert-level compliance guidance for ISO 27001, SOC 2, FedRAMP, GDPR, HIPAA, NIST CSF, PCI DSS, TSA Cybersecurity, and ISO 42001 AI Management System. Skills scored 94% vs a baseline of 72% without using Skills.

A curated list of Federated Learning papers/articles and recent advancements.

A curated list of data privacy and security resources

[NeurIPS23 (Spotlight)] "Model Sparsity Can Simplify Machine Unlearning" by Jinghan Jia*, Jiancheng Liu*, Parikshit Ram, Yuguang Yao, Gaowen Liu, Yang Liu, Pranay Sharma, Sijia Liu