Showing 1-20 of 22 projects
Curated list of open source libraries for deploying, monitoring, and scaling machine learning in production
An open-source audio recording and editing application that focuses on privacy and security.
Fawkes is a privacy-preserving tool against facial recognition systems, built using Python.
Rust implementation of OpenClaw focusing on privacy-preserving AI model execution and security hardening.
Insomnium is a fast, privacy-focused local API testing tool that supports GraphQL, REST, WebSockets, and gRPC.
An open-source data logging library for machine learning models and data pipelines.
Open-source, non-custodial, privacy-preserving Bitcoin wallet for multiple platforms.
A library for training PyTorch models with differential privacy, enabling privacy-preserving machine learning.
Nym is a decentralized, privacy-preserving network that enables anonymous transactions and strong network-level privacy.
A zkSnark circuit compiler for building privacy-preserving applications.
An IBM toolkit for computing on encrypted data without decryption, with demos for secure ML and key-value search.
A Python library for privacy-preserving machine learning using Fully Homomorphic Encryption.
An open-source privacy-preserving computing platform for secure multi-party computation and federated learning.
A GAN-based tool for anonymizing faces in images, useful for privacy-preserving computer vision applications.
A decentralized VPN written in Go that provides secure, privacy-preserving internet access.
A framework for encrypted machine learning in TensorFlow, enabling confidential computing and privacy-preserving AI.
An open-source, decentralized identity protocol built on top of Bitcoin, enabling secure and privacy-preserving digital identities.
A decentralized identity solution based on blockchain that follows W3C DID and Verifiable Credential standards.
Cryptosphere is an encrypted peer-to-peer web application platform for decentralized, privacy-preserving apps.
Versatile framework for secure multi-party computation with various privacy-preserving techniques.
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