Alessandro (@ctrlc03) on privacy-first infrastructure

Alessandro (@ctrl03) has spent years operating at the intersection of cybersecurity, applied cryptography, and Ethereum infrastructure. His work has focused on building and securing systems where privacy, coordination, and verifiability are fundamental requirements rather than optional features. From auditing production-grade environments to contributing to privacy-preserving infrastructure at the Ethereum Foundation, Alessandro has worked across zero-knowledge systems, distributed coordination protocols, and secure execution architectures. At the Interfold, he focuses on the cryptographic and distributed systems infrastructure to enable confidential coordination at scale.

• Name: Alessandro
• Role at the Interfold: Senior Engineer
• Area of expertise: Applied Cryptography, Software Engineering, Ethereum Smart Contracts
• Links: @ctrlc03 | site

Hi @ctrlc03! What’s your background, and what drew you to cryptography and confidential computation?

I started out in cybersecurity, spending several years testing and hacking systems ranging from small companies to banks. Over time, I got drawn to blockchain, initially as an auditor and later as a software engineer at the Ethereum Foundation’s Privacy Stewards of Ethereum team. There, I worked on projects like MACI, Semaphore, p0tion, and Excubiae.

After three years at PSE, I began looking for new opportunities, and the Interfold immediately caught my attention. The project’s focus on privacy and confidential computation resonated with me. It’s an area that can unlock applications previously impossible without compromising privacy. Being part of that mission was too exciting to pass up.

What inspires you most about working at the Interfold?

The complexity and scope of the project are incredible. Every day, I learn something new while collaborating with a team that’s always ready to help. Together, we’re building something genuinely useful that I can’t wait to see adopted.

What technical problem or area are you currently focused on?

Right now, I’m focused on voting use cases, improving CRISP, and building integrations with DAO stacks, such as the Aragon OSx plugin we recently completed. Working on the Interfold also gives me insight into challenges other developers might face, which helps guide improvements to the core protocol.

How do you see confidential computing evolving over the next five years?
As Fully Homomorphic Encryption (FHE) and other cryptographic primitives advance, I expect more projects to emerge, accelerating secure collaboration in various domains. With increasing demand and participation, we’ll likely see real-world applications of privacy-preserving technologies become more mainstream.

Any advice for developers or researchers interested in privacy-preserving technologies?
Don’t hesitate to dive in. Privacy technologies can be daunting at first, but they’re extremely rewarding. Start with zero-knowledge proofs, experiment with circuits, and then explore other primitives like MPC and FHE to build more complex applications.

One concept more people should understand: The differences between FHE, MPC, ZK, and TEEs.

Favourite tool/repo: The Interfold 😛

Favourite article/paper: On Collusion in Privacy Systems

Technical Spotlight: CRISP Vote Masking

I’ve been working on introducing a new concept to our voting protocol, CRISP, aimed at reducing the risk of coercion (like vote buying) and collusion through a technique we call vote masking. CRISP operates in the context of on-chain governance, where voters are identified by their Ethereum address and eligibility is determined via a token balance threshold (ERC20Votes). Each voter is assigned a slot in a Merkle tree, with each leaf representing an encrypted vote.

Here’s how vote masking works:

• Non-voters can submit an encryption of zero, which adds randomness but reveals no information about the vote itself.
• Eligible voters can submit a vote encrypted with FHE’s BFV scheme, proving ownership of their wallet via a ZK proof.
• This approach ensures that when a vote is submitted, it’s impossible to distinguish between an actual vote and a masked vote, providing voters with plausible deniability and reducing coercion risks.

Code: CRISP on GitHub