Cryptography tech fuelled by blockchain – ZKP, FHE, MPC, TEE
One key aspect of blockchain privacy is the trade-off between transparency and confidentiality. While transparency is a hallmark of blockchain, offering accountability and trust, it can compromise privacy in use cases like enterprise transactions and proprietary trading. This led me to explore and learn about technologies like Zero-Knowledge Proofs (ZKPs), Fully Homomorphic Encryption (FHE), Multi-party Computations (MPC) and Trusted Execution Environments (TEE).
Zero-Knowledge Proofs (ZKPs)
ZKPs allow a party to prove the validity of a statement without revealing any information about the statement itself. This is particularly useful in maintaining privacy in transactions. ZKPs enable the encryption of transaction details while still verifying their validity on the blockchain. For a deeper understanding, I recommend watching this 1-hour stream of Jim Zhang explaining the math behind it.
ZKPs can be applied to various aspects of our society. For instance, imagine a process of getting a bank loan at the moment – we take for granted the fact we need to expose everything about our lives (identity, property, other debts, cost of living, marital status, residence history, date of birth, etc) so the bank’s risk department can run this through an algorithm determining the outcome of our credit application. This doesn’t have to be this way. With ZKPs we could run the app on our private computer to generate a proof of credit score and then deliver that single QR code to the bank – they would scan it just to see if it’s a valid proof and we could get our loan without the bank even knowing our name. Sounds like science fiction. 😀
Another interesting application is proof of execution. Imagine I have a program I need you to execute on the data I specify (some data filtering, transformations or training a neural network). If there’s an economic incentive for you to act maliciously, i.e. pretending to have executed my program only to get paid, I would have to ask for a proof of execution before the bounty payout. That’s where ZKPs come into play, and companies are already working on solutions like Risc Zero, zkWASM to name just a few.
Fully Homomorphic Encryption (FHE)
FHE is a form of encryption that allows computations to be performed on encrypted data, producing an encrypted result that, when decrypted, matches the result of operations performed on the plaintext. This technology is revolutionary for shared private states, enabling complex operations on encrypted data without exposing the underlying information. This short article explains it quite well.
Potential application is enabling distributed computing in a trustless environment (unknown, third party servers) on a sensitive data, i.e. medical records or proprietary, hard to get dataset. It is currently very prohibitive due to the limiting set of operations which can be compiled from a regular code into an equivalent FHE circuit.
Multi-party Computations (MPC)
MPC is a cryptographic protocol that distributes a computation process across multiple parties, where no individual party can see the other’s data. It’s a powerful tool for collaborative computations on private data, ensuring privacy and security. This article provides a comprehensive overview.
Near Protocol has recently announced full on-chain signing as part of their effort to abstract away the complexities of working with individual chains. The end goal is easier user onboarding by making the underlying blockchain “invisible” to the user. This is possible because one MPC contract can securely store sensitive info such as private keys of any other chan, and use that to sign transactions in your name.
Personally, understanding the importance and the link between MPC and UX affecting the adoption rate was a quite heavy mental stretch. Once it clicked, I couldn’t stop learning.
Trusted Execution Environments (TEE)
An alternative (and a bit obvious approach) to secure computation is physical barrier, so called hardware enclave. The core idea is enabling untampered and private execution inside of closed environment guaranteed by the CPU manufacturer. Intel SGX and AMD Epyc processors support hardware enclaves and allow you to write software which can only be run inside of the enclave.
On the downside, enclaves have limitations in terms of program size and maximum available runtime memory, so they are still not suitable for a generic program execution.
What’s next?
I am passionately curious about the ZK technology fundamentals and how to effectively communicate the importance and the impact of it to builders around the world.
My journey has also involved participating in some hackathons, where some of the projects I did with my teams in blockchain privacy have gained some attention and rewards. I’ve been exploring if and how these technologies can be integrated into our platform at Coretex.ai to enable secure distributed computation and working comfortably with private datasets on a public platform.
As the world continues to push the boundaries of what’s possible in blockchain technology, I’m excited to share my learning progress and insights. The future is not just about blockchain, but about unlocking new applications in finances, healthcare, creator economy and many others. At the end of the day, it is about creating an ecosystem that is fair, self-sovereign, efficient, and accessible to all.