Fair Math

We are pleased to share the first edition of the FHERMA Cookbook — a collection of modular #FHE components and AI models, developed through fherma.io challenges. eprint.iacr.org/2025/1302 The Cookbook is designed as a practical, extensible resource for…

🧪 Open-Source Privacy Meets Agentic Infra @FairMath is a research company building open-source tools to tackle the world’s most complex privacy challenges — with a community-first ethos. They’re not just theorizing — they’re shipping privacy infrastructure for the age of…

Everyone’s excited about #FHE as the foundation for private computation in #Web3, and the technology is quickly approaching real-world adoption. But most protocols rely on a single global encryption key - a core design flaw that puts everything at risk. Here’s why 👇

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In this talk, I presented our approach and architecture for MLIR-based #FHE compiler and ecosystem designed for privacy-preserving computation using fully homomorphic encryption (FHE). youtube.com/watch?v=9dvVxb…

Fair Math FHE Computer enables encrypted, trustless, and scalable computation across blockchain, AI, and general-purpose workloads. If you haven't yet — take a look. Whitepaper: eprint.iacr.org/2024/1917 Docs: docs.fairmath.xyz

Stablecoins are foundational to crypto infrastructure — but lack built-in privacy. In our new article, we break down why existing solutions fall short, and what real-world adoption requires. Read it here: x.com/FairMath/statu…

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Happening tomorrow

Join us this Sunday for Agents Day at #EthCC in Cannes.

Meet our team in New York at Permissionless IV conference.

1/ Stablecoins have become one of the most practical and widely adopted components of crypto infrastructure. Yet without privacy, they remain fundamentally limited in real-world financial applications. Many approaches have been explored — but a robust, scalable solution is still…

Let’s catch up at DappCon

This summer, we are offering internships for students and early-career researchers interested in contributing to the privacy-preserving AI. Focus areas: - Designing and testing components for private model inference - Exploring FHE-based methods for secure computation To…

Buzzwords are easy. Building is harder. 🔹 @antongotchi — founder of @sleepagotchi 🔸 @misk0vic — cofounder of @cytonic_com 🔹 @g_arakelov — founder of @FairMath Join us May 28 at 3PM UTC as we go beyond the fluff, anon. Set your reminders: x.com/i/spaces/1ynJO…

Privacy 2.0 Market Map (v2) The Privacy 2.0 landscape can be split into 3 key PETs, enabling private shared state: - FHE (Fully Homomorphic Encryption) - MPC (Multi-Party Computation) - TEEs (Trusted Execution Environments) If you know of any other projects that belong in…

We’re getting very close to making Privacy-Native AI real — for everyone. Stay tuned.