The Ethereum Virtual Machine (EVM) serves as the beating heart of the Ethereum network, acting as the runtime environment for executing smart contracts and decentralized applications (dApps). The EVM's importance goes beyond Ethereum, as many layer-2 (L2) solutions aim to be compatible with it. This is mainly because of Ethereum's strong developer base and active community.
EVM and Ethereum can be seen as basic technologies that support many other web3 projects. The smart contract system was initially built as a transparent and trustworthy decentralized alternative to traditional web protocols. Since the launch of Ethereum, the ecosystem has faced new challenges. For example, it can't hide sensitive data in the contracts. Transactions on public blockchains are visible to everyone, which could expose sensitive information about the people involved.
However, new EVM-based innovations are developing ways to improve privacy on the blockchain. These methods use complex cryptography and off-chain techniques to protect transaction details and private information.
Solutions for Blockchain Privacy: the FHE Perspective
Solutions leveraging different cryptographic primitives including: Zero-Knowledge Proofs (ZKPs), and Secure Multi-Party Computation (MPC) are transforming privacy on the blockchain. ZKPs, utilized by projects like Aztec and Zether, ensure confidential transactions by obscuring transaction amounts and participant identities. Secure Multi-Party Computation (MPC) protocols, such as those by Enigma and Keep Network, enable privacy-preserving smart contracts by executing computations on encrypted data.
Another promising avenue for enhancing privacy in blockchain lies in the development of the FHE-EVM, which integrates Fully Homomorphic Encryption (FHE) into blockchain technology. This innovative approach allows smart contracts to execute operations on encrypted data without necessitating decryption at any stage.
Zama has introduced a protocol for building the FHE-EVM, which companies like Fhenix and Inco have adopted for their solutions. Conversely, research company Fair Math has proposed a distinctive approach that underscores a collaborative strategy for building an FHE-EVM. This approach is pivotal in tackling challenges in the zk field, where limited development tools often result in wasted resources and abandoned projects. By fostering collaboration, Fair Math aims to mitigate these risks and accelerate progress in FHE solution development. Fair Math's competitive model ensures that the solution evolves naturally over time, removing the requirement for constant development efforts.
A Closer Look at FHE-(E)VM Ecosystem
Building FHE projects collaboratively requires key blocks to speed up the process. Fair Math initiated this by partnering with OpenFHE to create FHERMA, an FHE challenges platform. Within FHERMA, there's a dedicated track where winning solutions are incorporated into the component library. The aim is to build the initial version of the FHE-(E)VM through ongoing competition, with new and improved components replacing existing ones over time.
FHERMA isn't just about FHE-(E)VM challenges, though; it's a hub for various FHE contests, inspiring innovation in encrypted data processing. By providing support for various encryption schemes, participants are encouraged to explore different methods for solving challenges and to expand the possibilities of encrypted data processing. Challenges range from privacy-preserving machine learning to algorithms for extracting data from encrypted containers.
FHE computations are resource-intensive, so the platform connects developers with computation providers, or “actors”, to streamline the process. This approach democratizes access to FHE, making it easier for developers to integrate advanced encryption into their projects. By offloading complex calculations to actors, developers can focus on improving their applications.
Exploring the Promise of Collaborative Approach
Fair Math has opted for a modular approach in designing the FHE-(E)VM. Essentially, its core consists of a set of operation codes for working with encrypted data. Thus, the FHE-EVM can be viewed as an entity comprised of a collection of building blocks, each corresponding to an operation code.
The approach proposed by Fair Math involves utilizing its platform for FHE challenges to create a space for the continual evolution of what is termed collaborative FHE-EVM. The main idea is that anyone can attempt to enhance a specific block of the FHE-EVM. To do so, one simply needs to upload their solution to the relevant challenge, and if the solution proves to be the best, it becomes part of the EVM.
Closing Remarks
Providing an open, secure, and accessible environment for developers across diverse backgrounds, the FHE-(E)VM initiative seeks to drive the adoption of advanced encryption techniques and catalyze innovation in both Web2 and Web3 realms. Overall, the proposed approach is highly intriguing and has the potential to pave the way for the development of similar innovative projects. The principle built on openness and competition instills hope for the best solution, as excellence often arises from competition.
Disclaimer: This article is provided for informational purposes only. It is not offered or intended to be used as legal, tax, investment, financial, or other advice.
