Podcast: Verifying Onchain Data for Data Interoperability w/ Xiang Xie, Primus

This week on the FHE Onchain podcast, we sat down with Xiang Xie, co-founder and CEO of Primus. Xiang is a cryptographer with an extensive background in FHE, ZK, and MPC, and he’s been working on privacy-enhancing cryptographic solutions for many years. With Primus, he’s aiming to make confidential computing both scalable and verifiable.

In our conversation, we covered everything from verifiable FHE to zkTLS, and why privacy solutions need to be practical for real-world adoption. If you haven’t tuned in yet, don’t miss out!

What Is Primus Building?

Primus is taking a unique approach to confidential computing. Instead of just developing FHE solutions, it’s also tackling the verifiability problem—a major missing piece in FHE today.

The core of their work includes:

• zkTLS: A cryptographic framework for bringing Web2 data into Web3 in a verifiable way.
• Verifiable FHE (vFHE): Allowing computations over encrypted data while ensuring correctness without needing to trust the operator.
• A Compiler for FHE: Making it easier for developers to write code in high-level languages like Python and seamlessly convert it to FHE-compatible operations.

Why Verifiable FHE Matters

One of the biggest issues in confidential computing is ensuring that computations are being done correctly. Standard FHE allows encrypted computations but still requires trust in the entity performing them. Verifiable FHE (vFHE) solves this by using zero-knowledge proofs (ZKPs) to prove that operations were carried out correctly.

Xiang and his team have been working on this for years. They’ve developed an approach that enables bootstrapping proofs in under 10 seconds—a massive speedup compared to existing methods. This is a huge step towards making verifiable FHE practical.

Scaling Confidential Compute

Confidential computing has traditionally been seen as too slow or too expensive to scale. But Primus is tackling these challenges head-on with a few key innovations:

• zkTLS for Data Ingestion: Before confidential computing can take off, we need more data in Web3. zkTLS helps securely bring Web2 data onchain.
• Optimized vFHE Bootstrapping: Their recent breakthroughs allow verifiable computations to be done much faster, making FHE more usable.
• FHE Compiler: By letting developers write in Python and automating the cryptographic heavy lifting, Primus is lowering the barrier to entry for confidential computing.

Will People Actually Use Privacy Tech?

A common question in Web3 is whether users actually care about privacy. Xiang’s take on this is the fact that privacy isn’t something users should have to think about—it should be built-in.

Rather than expecting mass adoption to be driven by ideology, the real opportunity lies in practical applications:

• Private AI models
• Encrypted payroll systems
• MEV-resistant DeFi
• Confidential order books

In Xiang’s view, the problem isn’t demand—it’s supply. Solutions haven’t been efficient or developer-friendly enough. But that’s changing fast.

Final Thoughts and A Book Recommendation

Before wrapping up, we asked Xiang for a book recommendation, and he suggested "Programmable MPC"—a great resource for cryptographers and engineers looking to build privacy-preserving applications.

This was an incredibly insightful conversation. If you want to understand where FHE, MPC, and ZK are headed, Primus is one to watch.

Full episode ⬇️

https://youtu.be/o_9rd4UQJLU