Editors note: Fully Homomorphic Encryption (FHE) is a technology that can process data without decryption. This means companies can provide services without seeing user data, and users wont notice a difference in functionality. Because data is encrypted during transmission and processing, network behavior is end-to-end encrypted. In other words, FHE allows for better implementation of zero trust, which can be shared on untrusted domains and the data cannot be read by the person performing the calculation. ,
Industry concept
Zama, a leader in the FHE industry, recently published an article about its Master Plan. The article announced that the company successfully raised $73 million (valuation was not disclosed) and outlined the companys vision to create an end-to-end encrypted network HTTPZ (Z means Zero Trust, zero trust).
Four years old, Zama has advanced FHE from theoretical mathematics to real code, thereby improving developer accessibility and expanding FHEs application scope. Currently, Zamas FHE library suite can support end-to-end encryption applications in various industries, and has also greatly improved the speed of FHE solutions. Its launch of fhEVM, a confidential smart contract solution, solves privacy issues in blockchain transactions. Zama believes that FHE has many potentials in blockchain applications, including privacy tokens and decentralized identity (DID), emphasizing that FHEs application in artificial intelligence will have a wider impact in the future.
Several FHE builders in Web3 share Zamas goals and are pushing to make them a reality.
This article will share the views of the founders of Mind Network, Fhenix, and Inco, three popular projects on the FHE track, and explain how they implement end-to-end encrypted networks in Web3 and why these projects will fundamentally change the way users interact with the network. , and why they think the application scenarios of FHE are promising.
Mind Network
Mind NetworkIt is the first universal Restaking Rollup solution based on FHE, providing secure computing and consensus for EigenLayer and the Ethereum ecosystem.
Crypto AI and DePIN still need to solve some difficult problems to defeat their Web2 competitors. In cryptographic AI, if other validators can replicate predictions, then the system intentionally reduces the amount of computation but still earns token rewards for verification, thereby reducing network security. Therefore, encrypting the output is key.
Another challenge facing crypto AI is how to launch a decentralized verification network. EigenLayer provides the market with a service for this problem, allowing security to be shared through ETH and liquidity staking tokens. But at the same time, artificial intelligence has higher requirements for the security of consensus calculations and data security. This is another key challenge that AI systems need to solve.
On the DePIN issue, users receive token rewards by contributing specific data, but they also inadvertently expose important data such as device, location, and income to the network. If DePIN becomes the industry standard for todays IoT, Web3 users will have worse privacy than those in the Web2 model. This is a key challenge that DePIN aims to solve.
Mind Network provides solutions to solve the above problems. Mind Network cooperates with ZAMA to implement verifiable decentralized computing on encrypted data and provides FHE-based data security, computing security and consensus security solutions, thus solving the first problem mentioned above. Secondly, Mind Network expanded EigenLayers consensus service to meet the needs of artificial intelligence computing, thus realizing the key to artificial intelligence networks - probabilistic consensus. This solution will provide Restaker with more benefits from artificial intelligence networks. At the same time, Mind Network provided the FHE bridge solution for Chainlink CCIP, which also received a Grant from the Ethereum Foundation.
At present, Mind Networks artificial intelligence solutions have reached preliminary product market fit with projects such as IO.Net, AIOZ, Nimble, AigentX, Chainlink, Connext and Akash, and have received 600,000+ active users in the latest test network activities Wallets participate.
Fhenix
Since its inception, Ethereum has chosen to trade data integrity for confidentiality. Users can trust Ethereum when it comes to adhering to the rules of the system, for example, keeping financial accounts honestly. But when it comes to sensitive information, users are completely unable to maintain the same level of trust.
This dichotomy greatly limits the types of use cases that Ethereum can handle. In fact, if Ethereum is to truly develop into"Web3", users need to ensure that Ethereum can not only do what the current network can do, but also do it better. Take the poker game as an example - although it is believed that Ethereum will not cheat, it cannot allow each player to hide their cards from each other. If this is not possible, the game cannot be played at all.
Only by solving the problem of on-chain confidentiality can such applications be realized, and this is where FHE comes in. Fhenix uses and extends Zamas encryption library to build a FHE coprocessor. The FHE coprocessor is an extension of Ethereum (L1, L2, or L3) that allows applications to outsource specific calculations that require processing sensitive data. For example, a DAO governance mechanism could run a private voting mechanism that lets people encrypt their votes and then have a coprocessor tally them (on encrypted data) while only revealing the final result.
Fhenixs FHE coprocessor technology is based on the lightweight FHE Rollup architecture, which greatly improves scalability. Assuming that each chain is equipped with such a coprocessor, it can promote the emergence of countless new applications. Fhenix believes this will be the catalyst for more than a billion users to flock to cryptocurrencies.
Inco
Inco is an EVM-based Layer 1 blockchain, secured by Ethereum via EigenLayer, and simplifies the complexity of FHE, enabling developers to use the most commonly used smart contract language Solidity and tools in the Ethereum ecosystem such as Metamask, Remix and Hardhat) to build a confidentiality DApp in 20 minutes.
Additionally, similar to how Celestia provides Data Availability (DA) for Ethereum and other blockchains, Inco serves as a modular confidential computing network that extends confidentiality by providing confidential storage, computation, and access control. Ethereum and other public L1 and L2.
For example, a trustless on-chain game could be developed on Arbitrum with most of its core logic hosted on Arbitrum, while Inco is dedicated to storing hidden information (such as cards, player states or resources) or performing private computations (such as payments, voting or stealth attacks). Inco’s goal is to bring confidentiality to the value layer of the Internet and drive the next phase of mass adoption.
end-to-end
The founders believe that an end-to-end encrypted network is the only potential solution to the networks most critical problems, and it could take four, or it could take eight years to achieve this goal. However, the zero-trust infrastructure implemented by FHE brings reasonable and mandatory privacy protection and consensus security to transactions and data, helping to bring DePIN and decentralized artificial intelligence to the public.
Looking ahead: The implications of fully homomorphic encryption
Fully Homomorphic Encryption (FHE) is a cryptographic"holy grail", and is also the key to protecting privacy and meeting security needs in contemporary times. Its origins can be traced back to the concept first proposed by Rivest, Adleman and Dertouzos in 1978. However, it was not until 2009 that Stanford PhD candidate Craig Gentry realized this vision with a groundbreaking paper that provided the first feasible FHE solution.
This technology enables complex calculations to be performed on encrypted data without the need for decryption, providing a solution where data remains secure and private even during analysis, a process known as creating a shared private state (Create shared private state). In the past few years alone, advances in FHE have significantly increased efficiency and usability, moving it from a theoretical concept to a practical tool for secure data processing.
Today, FHE has become the cutting-edge technology of Web2 network security and is widely used in the fields of cloud computing and data analysis. In these areas, sensitive information must be protected without compromising the ability to extract valuable insights. Web2 already has strict privacy protections in place and, despite being centralized, is still vulnerable to attacks. Web3 was originally built for public data, which is a key challenge that the Web3 ecosystem needs to solve. If Web2 became Web3 tomorrow, our grocery bills, app subscriptions, phone bills, etc. would all become public information. Solving confidentiality issues in Web3 is crucial. FHE or users will be able to implement powerful solutions to enhance privacy and security in the future, allowing operations on encrypted transactions, data and smart contracts while maintaining confidentiality.
Among the three methods of Zero Knowledge Proofs, Multi-Party Computation and Fully Homomorphic Encryption FHE, FHE is the cornerstone. These three methods constitute a new vertical field in Web3: decentralized confidential computing ( Decentralized Confidential Computation—DeCC). DeCC will greatly expand the use cases of Web3 and make Web3 widely adopted.
