Privacy is No Longer a Tradeoff

Compute over private data without revealing it - powered by Stoffel's MPC stack.

Backed by

  • ANNA ROSE

  • MALLESH PAI

  • DAVID WONG

  • NICOLA GRECO

  • ALEX OBADIA

  • JOHN ADLER

  • SAIF AKHTAR

  • ZHENG LEONG CHUA

  • YUKI YUMINAGA

  • VISHESH CHOUDHRY

  • ANDREW MILLER

  • EVAN VAN NESS

  • ANNA ROSE

  • MALLESH PAI

  • DAVID WONG

  • NICOLA GRECO

  • ALEX OBADIA

  • JOHN ADLER

  • SAIF AKHTAR

  • ZHENG LEONG CHUA

  • YUKI YUMINAGA

  • VISHESH CHOUDHRY

  • ANDREW MILLER

  • EVAN VAN NESS

What is MPC?

(and Why it Matters)

Multi-Party Computation (MPC) keeps sensitive data private—even during computation.

Private by Design

No one sees the full data, not even the computation parties.

No Single Point of Trust

Eliminates data leaks by decentralizing computation.

Built for Real-World Use

MPC has been battle-tested in production since 2008 by major companies including Facebook, Google, and Revolut.

We Handle the Heavy Lifting. You Focus on Building.

We Handle the Heavy Lifting. You Focus on Building.

We Handle the Heavy Lifting. You Focus on Building.

Encrypted data isn't useful if you can't compute on it. We solved both problems at once.

Stoffel Lang

Stoffel Lang

Stoffel Lang

Write Code, Not Circuits

Skip cryptography. Stoffel Lang lets you focus on application logic, while we handle the privacy math.

Coming soon.

Stoffel VM

Stoffel VM

Stoffel VM

Blazing-Fast MPC Computation

Our Stoffel VM is purpose-built for speed, delivering high-performance MPC at scale.

Coming soon.

MPC vs. The World

Multiple approachs protect privacy, but each serves different needs:

MPC (Multi-Party Computation)

Computes private data without revealing it. Perfect for interactive applications that need privacy at runtime (e.g., trading, auctions, coordination).

Use MPC when: You need to process private data and control when (or if) it becomes public.

ZK (Zero-Knowledge)

Proves something is true without sharing the data. Best for verifying identity, ownership, or correctness after computation.

Use ZK when: You need to prove something happened without exposing the details.

MPC (Multi-Party Computation)

Computes private data without revealing it. Perfect for interactive applications that need privacy at runtime (e.g., trading, auctions, coordination).

Use MPC when: You need to process private data and control when (or if) it becomes public.

ZK (Zero-Knowledge)

Proves something is true without sharing the data. Best for verifying identity, ownership, or correctness after computation.

Use ZK when: You need to prove something happened without exposing the details.

MPC (Multi-Party Computation)

Computes private data without revealing it. Perfect for interactive applications that need privacy at runtime (e.g., trading, auctions, coordination).

Use MPC when: You need to process private data and control when (or if) it becomes public.

ZK (Zero-Knowledge)

Proves something is true without sharing the data. Best for verifying identity, ownership, or correctness after computation.

Use ZK when: You need to prove something happened without exposing the details.

How Privacy Technologies Work Together

MPC

+

ZK

Prove your computation was correct without revealing the data.

MPC

+

FHE

Process sensitive data across multiple services while keeping it encrypted.

MPC

+

TEE

Speed up secure multi-party computation using trusted hardware.

Imagine What Privacy Could Enable

From reshaping how markets deal with front-running to how healthcare shares patient data - what will you build with Stoffel?

Trading & Marketmaking

Run truly sealed-bid auctions. Prevent front-running in trading platforms through private order matching.

Social & Marketplaces

Dating apps where preferences stay private until there's a match. Game platforms where player strategies remain confidential until played.

Research & Healthcare

Collaborate on medical research while keeping patient data private. Match records across healthcare systems while maintaining compliance.

Enterprise

Compare customer databases without revealing full lists. Match salary data across companies while keeping individual compensation private.

See Our Progress

Check out our latest code, commits, and development updates

We’re gathering a community of builders, researchers, and privacy advocates shaping the future of private applications.