Countly is a product analytics platform that helps teams track, analyze and act-on their user actions and behaviour on mobile, web and desktop applications.
#自然语言处理#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.
#计算机科学#Diffprivlib: The IBM Differential Privacy Library
Awesome Machine Unlearning (A Survey of Machine Unlearning)
OpenHuFu is an open-sourced data federation system to support collaborative queries over multi databases with security guarantee.
#计算机科学#All materials you need for Federated Learning: blogs, videos, papers, and softwares, etc.
#计算机科学#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.
#计算机科学#An easy-to-use federated learning platform
Filter sensitive information from free text before sending it to external services or APIs, such as chatbots and LLMs.
The modern spam protection. Protects your forms from spam by simply checking the content. Open source, Free to use, Accessible, and Self-Hosted.
#区块链#A permissionless blockchain network to manage digital identity and access rights
#安全#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, Denn...
#自然语言处理#VeritasGraph: Enterprise-Grade Graph RAG for Secure, On-Premise AI with Verifiable Attribution
#Awesome#A curated list of Federated Learning papers/articles and recent advancements.
A curated list of data privacy and security resources
Statify – statistics plugin for WordPress
[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