Pyte’s Groundbreaking Multi-Party Computation Offers Faster Training, Privacy Compliance, and Secure Collaboration with Sensitive Data

Read the latest Gradient Flow newsletter to learn why SMPC is the answer to fully exchanging data while protecting privacy – and why Pyte in particular is a prime example of groundbreaking multi-party computation, paving the way for new uses of collaboration with AI and much more.

SMPC is “akin to a group of people solving a puzzle together without revealing their individual pieces,” writes Gradient Flow author Ben Lorica. It’s strength comes of being “cryptography that enables joint computation of a function while keeping private inputs hidden, even during collaboration.”

“Pyte,” Lorica explains, “offers scalable and performant solutions that allow teams to work on real datasets without exposing sensitive information. Their tools, designed for compliance and governance, ensure data confidentiality and prevent leaks and copyright issues.”

To read why Lorica sees Pyte solving previously unanswered challenges of data privacy, and why he sees Pyte’s solutions as particularly useful for bringing businesses together to forge new opportunities in AI. See the whole post on Substack.