AI and software engineer building privacy-preserving technology.
π PySyft - Perform data science on data that remains in someone else's server
π‘ Syft Client - Peer-to-peer data science and AI via channels your organization already trusts
π§ͺ Private ML - Machine Learning with SPDZ
π§° Toolbox - A tool for downloading your data and using it via CLI's
π¦ pymemri - Python SDK for building plugins on the Memri personal data platform
legacy
ποΈ pod - The open-source backend for Memri
legacy
π pyintegrators - Import, enrich, and act on personal data from Gmail, WhatsApp, and more
legacy
π± ios-application - The iOS front end of Memri
legacy
π browser-application - The browser front end of Memri
legacy
Three Tools for Practical Differential Privacy - Practical tools for making differentially private ML actually work: sanity checks, adaptive clipping, and large-batch training. link
Syft 0.5: A Platform for Universally Deployable Structured Transparency - A framework combining privacy-enhancing technologies for universally deployable structured transparency. link
Training a CNN using SPDZ - How to train convolutional neural networks using secure multi-party computation with 6x reduced communication overhead. link
Secure Enclaves for AI Evaluation - How OpenMined, UK AISI, and Anthropic used secure enclaves to evaluate AI models while protecting proprietary assets. link




