Building the Master Plan

Zama, the leader in the FHE industry, recently published its Master Plan article. This article announced its successful raise of $73M (undisclosed valuation) as well as outlined its ambition of creating an end-to-end encrypted web, called HTTPZ (Z for Zero Trust).

Four years since its founding, Zama has progressed FHE from theoretical math to practical code, enhancing accessibility for developers and widening FHE's application. Their suite of FHE libraries now enables end-to-end encrypted applications across various industries, significantly improving the speed of their FHE scheme. Zama's introduction of the fhEVM, a confidential smart contract solution, addresses privacy in blockchain transactions. They also highlight potential blockchain applications for FHE, including confidential tokens and decentralized identity, and the role of FHE in AI, foreseeing broader future impacts.

There are a handful of FHE builders in Web3 who believe in Zama's goal and are pushing to make it a reality.

In this article, you will read the founders of Mind Network, Fhenix, and Inco state how they are helping to realize an end-to-end encrypted web specifically in Web3, and why these projects will fundamentally change the way users interact with the web. They have extensively discussed these topics during the weekly MindChats on X, hosted by Mind Network.


Mind Network

Mind Network is the first Fully Homomorphic Encryption (FHE) based general-purpose Restaking solution, bringing secure computation and consensus to the EigenLayer and Ethereum ecosystems.

Crypto AI and DePIN have a few challenges to solve in order to grab market share from their Web2 counterparts. In crypto AI, if other validators can copy predictions, there is a systemic incentive to reduce computational output but still earn token rewards for validation, thereby lowering network security. So encrypting outputs is key. Another challenge for crypto AI is in bootstrapping a decentralized validator network. EigenLayer has brought a key service to this market by allowing for shared security through ETH and liquid staking tokens. But AI computation needs more granularity than regular crypto transactions because AI computation is more complex. This is another key challenge to solve for AI systems.

On the matter of DePIN, users mining their data for token rewards are sharing incredible data to the network about their devices, their geo-location, and their earnings. If DePIN became the industry standard for IoT today, Web3 users would have less confidentiality than in the Web2 paradigm. This is a key challenge to solve for DePIN, as noted by Vitalik and many other industry leaders.

How does Mind Network address these problems? Mind Network uses Zama’s FHE library to enable verifiable decentralized computation over encrypted data, which solves the first issue mentioned. Secondly, they extend EigenLayer’s consensus service to meet AI computation needs, allowing for probabilistic consensus, and reducing the validator copying issue. The prelaunch campaign on Galxe is well received by the community. 

Fhenix


From its inception, Ethereum has traded data integrity for confidentiality. On the one hand, we can trust Ethereum when it comes to following the rules of the system; for example, keeping an honest account of a financial ledger. On the other hand, we can’t trust them at all with sensitive information.

This dichotomy greatly limits the kind of use cases that Ethereum can handle. In fact, for Ethereum to actually evolve into “Web3”, we need to make sure that they can do what the web does today – but better. Consider for example a game of poker – while Ethereum can be trusted not to cheat, it cannot keep each player’s cards hidden from each other, and without that capability – the game cannot be played at all.

The only way to enable such applications is if we solve the problem of on-chain confidentiality. This is where FHE comes in. At Fhenix, we’re using and extending Zama’s cryptographic libraries to build an FHE Coprocessor. An FHE Coprocessor is an extension to Ethereum (L1, L2, or L3), where an application can outsource specific computations that require handling sensitive data. For example, a DAO governance mechanism can run a private voting mechanism by letting people encrypt their votes and have the coprocessor perform the tally (over the encrypted data), while only revealing the final result.

Fhenix’s FHE coprocessor technology is based on a lightweight FHE rollup architecture, which greatly improves scalability. When every chain is equipped with such a coprocessor, countless new applications will emerge. We firmly believe that this will inevitably be the catalyst which will onboard a billion+ users into crypto.

Inco

Inco is an EVM-based Layer-1 blockchain, secured by Ethereum through EigenLayer, and abstracts away the complexity of FHE, enabling developers to build confidential DApps in 20 minutes by using the most adopted smart contract language, Solidity, and toolings from the Ethereum ecosystem, such as Metamask, Remix, and Hardhat.

In addition, similarly to how Celestia provides Data Availability (DA) to Ethereum and other blockchains, Inco, as a modular confidential computing network, extends confidentiality to Ethereum and other public L1s and L2s by providing confidential storage, computing, and access control. For instance, a trustless on-chain game can be developed on Arbitrum, with most of its core logic hosted there, while utilizing Inco exclusively for storing concealed information (e.g., cards, player stats, or resources) or performing private computations (e.g., payments, voting, or hidden attacks). Inco aims to bring confidentiality to the value layer of the internet, and push for the next frontier of mass adoption.

End-to-End

An end-to-end encrypted web is the only potential future of the web that solves its most critical problems. Zero trust infrastructure, enabled by FHE, brings a reasonable and mandatory level of privacy to transactions and data, helps bring Depin to the masses, and helps decentralized AI beat centralized AI. The FHEuture is very bright.

Looking for the future: The Significance of Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) is the 'Holy Grail' of cryptography and the key to preserving privacy and meeting security needs in our current time. Its origins trace back to a concept first proposed by Rivest, Adleman, and Dertouzos in 1978. However, it wasn't until 2009 that Craig Gentry, a Stanford University Ph.D. candidate, realized this vision through a groundbreaking dissertation that provided the first feasible scheme for FHE.

This technology allows complex computations to be performed on encrypted data without the need for decryption, thus offering a solution where data can remain secure and private even during analysis. We call this process 'creating a shared private state.' In just the last few years, advancements in FHE have significantly improved efficiency and usability, transforming it from a theoretical concept into a practical tool for secure data processing.

Today, FHE is the cutting edge of cybersecurity in Web2, enabling a wide range of applications in cloud computing and data analysis where sensitive information must be protected without hampering the ability to extract valuable insights. Web2, however, already has strict privacy in place, albeit centralized and vulnerable to attack. Web3 was originally built for public data. This is a key challenge for Web3 ecosystems to solve. If Web2 became Web3 tomorrow, our grocery bills, app subscriptions, phone bills etc would all be public information. Solving this confidentiality problem in Web3 is critical and urgent. FHE is sorely needed as a way to enhance privacy and security and allow for operations on encrypted transactions, data, and smart contracts.

FHE is the keystone of three methods (Zero Knowledge Proofs, Multi-Party Computation) that comprise a new vertical within Web3; Decentralized Confidential Computation (DeCC). DeCC will greatly expand use cases within and mass adoption of Web3.


Co-authors: 

Christian Pusateri from Mind Network, 

Guy Itzhaki and Mak from Fhenix,

Remi Gai from Inco.