A few weeks ago, we announced @EnclaveE3—a protocol for encrypted execution environments (E3).

It grew from our efforts to solve for secret ballot, but has evolved into something much broader, tackling deeper trust and privacy challenges in web3.

A few weeks ago, we announced @EnclaveE3—a protocol for encrypted execution environments (E3).

It grew from our efforts to solve for secret ballot, but has evolved into something much broader, tackling deeper trust and privacy challenges in web3.

https://x.com/EnclaveE3/status/1841502752674828324

Commit-reveal schemes are common but come with a major flaw: votes are private only temporarily. While this prevents bias when casting votes, it leaves the door open for collusion, as votes eventually become public.

That was the first trade-off we wanted to address.

@zkMACI offer “receipt freeness,” making it impossible to prove how someone voted, but relies on a trusted operator (the coordinator) to tally the votes and guarantee privacy. This creates a central point of trust, which we wanted to eliminate to stay true to web3 principles.

Similarly, systems based in Trust Execution Environments (TEE) offer guarantees of privacy and integrity based in apparently secure hardware, which ultimately boils down to trust in the supply-chain, setup, and physical access to that hardware; trusted operators.

Enclave uses the combination of fully homomorphic encryption (FHE), zero-knowledge proofs (ZKP), and threshold cryptography. With @EnclaveE3, no single trusted party has access to inputs, and inputs remain private, even after the final result is published.

The real breakthrough came when we realized that this architecture could extend beyond just voting. @EnclaveE3 is a protocol that allows secure computations on private data controlled by multiple parties across a range of applications, while maintaining privacy throughout the entire process.

For example, @EnclaveE3 could power sealed-bid auctions, where all but the winning bid remain encrypted—hidden from everyone—but the result is computed transparently.

It could also enable secure research over private medical data, where sensitive information is never exposed.

Another use-case @EnclaveE3 could enable is AI models to be trained or fine-tuned on data contributed by many mutually distrusting parties without ever revealing their raw data, preserving privacy while benefiting from large-scale data inputs.

What sets @EnclaveE3 apart is that it distributes trust across a decentralized network. Rather than relying on a single trusted intermediary to ensure privacy, Enclave uses a network of node operators with economic incentives to secure the data and process it correctly.

@EnclaveE3 doesn’t just offer privacy—it disintermediates complex systems that have traditionally relied on trusted data custodians. People will be able to engage with complex systems involving their private data, without placing trust in a single party for its security.

Like blockchains, and the internet before them, @EnclaveE3 disintermediates people and the systems they interact with. Empowering use of these systems with with sensitive data, but with full confidence that their private inputs remain private.

We're incredibly excited about what this unlocks: a future where applications from voting to large-scale data analytics are built with privacy at their core. The potential is huge, and we believe @EnclaveE3 will dramatically improve how data is handled in web3 and beyond.

Follow @EnclaveE3 and stay tuned as we continue to explore new use cases and expand Enclave's capabilities. We're just getting started on this journey, and there’s so much more to come!

The original problem we set out to solve was creating a web3-native way for private voting. Existing solutions weren’t sufficient—they either exposed votes before tallying or required trusted operators, both of which conflicted with our goals of privacy and decentralization.