Stoffel

A privacy-preserving compute stack enables you to perform analytics, AI/ML steps, matching, and key operations on sensitive data where it resides, without moving or exposing the raw records, so data stays in place and you receive only the computed results. Its features include a high-level language to write computations, a compiler that marks data as private or public with explicit reveals, local simulation and unit testing, a production VM that coordinates multi-party runs with predictable latency, retries, and checkpoints, and SDKs for Rust, Python, and JavaScript plus domain adapters to drop computations into your existing stack, enabling private queries over public databases, federated learning with only aggregated updates, and asynchronous distributed key generation for solid key management—tailored for data scientists, ML engineers, developers, analytics teams, and privacy-first organizations needing secure collaboration and data sovereignty.

Stoffel può essere trovato in Serverless Computing Platforms categorie.