Author: Biteye, Core Contributor Fishery Isla

Editor: Biteye, Core Contributor Crush

For Ethereum and the broader blockchain narrative, there are already many excellent teams launching scaling solutions, and scaling is not the only problem that needs to be solved.

The next key feature to be implemented is privacy, and the privacy track has recently become a hot spot for primary market infrastructure investments.

This article will introduce the implementation of two hot privacy chain technologies, Zero Knowledge Proof and Fully Homomorphic Encryption, and will also introduce potential projects worth paying attention to.

First, let's discuss a question: Does Web3 really have a use case for privacy?

01. Why does Web3 need privacy?

Existing mainstream chains are public ledgers, and all transactions are conducted on the chain, which means that the state changes containing asset information related to addresses or accounts are publicly transparent.

Initially, information transparency was only set as an accompanying feature for supervising consensus security. With the development of the industry, the consensus mechanism has gradually been optimized and improved to be reliable, and the gradually open and transparent public ledger has become a feature serving for technical arbitrage:

Miners can selectively package transactions based on fees, leading to lower fee transactions being less likely to be processed, thereby forcing users to increase gas fees. More worrisome, miners or block producers conduct front-running and censorship attacks by monitoring the public ledger.

By monitoring buy orders on the chain and adding their own buy orders before retail buy orders are executed, this has led to significant security issues. In the past year, MEV has successfully extracted nearly $2 billion from the market.

Such a huge and continuous outflow of funds is a great hazard on the development path of the crypto market.

At the same time, without privacy support, users lose ownership of their data. Address asset information and transaction information are both subject to monitoring and use. This goes against the vision of Web3.

Therefore, after the scaling problem is solved, a privacy smart contract chain becomes the next urgently needed feature to be implemented.

To achieve privacy smart contracts, three technical routes are currently being adopted:

1) The TEE (Trusted Execution Environment) solution represented by the online but lukewarm Secret Network and Oasis Network;

2) The zkVM solution derived from the ZK (Zero Knowledge Proof) principle that has entered the public eye through Ethereum zk-rollup;

3) The FHE (Fully Homomorphic Encryption) solution that has only recently entered the market's view.

TEE technology is the most mature, with many related documents. Interested readers can also learn about it on their own or experience the projects mentioned above. Therefore, this article will focus on the more topical zkVM and FHE solutions.

02. Zero Knowledge Proof (ZKP)

zkEVM and zkVM

Most ZK solutions are divided into two major camps: one is built on top of Ethereum (zkEVM), and the other is custom-built (zkVM), so they may choose to build a different set of underlying trade-offs and basic parameters.

zkEVM is a virtual machine compatible with the Ethereum Virtual Machine (EVM) and friendly to zero-knowledge proofs, ensuring the correctness of programs, operations, inputs, and outputs.

By building on top of the Ethereum blockchain, the zkEVM model absorbs the advantages and disadvantages of Ethereum.

Because it optimizes compatibility with the Ethereum network, it benefits from Ethereum's large user base, and developers find it easier to develop on this basis (this is because the number of Solidity developers is large, and its infrastructure, including execution clients, is shared).

However, this also means that its ability to integrate zero-knowledge proofs and other privacy measures is limited to the built-in restrictions of the Ethereum network.

The closer the zkEVM model is to fully simulating the Ethereum model, the more performance costs it incurs, as generating proofs takes longer.

Since all computations are completed on the blockchain, every transaction is completely publicly transparent, which is advantageous for some applications, but for others, this lack of privacy is unreasonable or insecure (e.g., applications related to sensitive personal financial information).

zkVM is a virtual machine that ensures security and verifiability through zero-knowledge proofs—you input the old state and program, and it returns the new state in a trustworthy manner. It can optimize the environment to lower the cost, make it more effective, and even easier to integrate zero-knowledge proofs into on-chain transactions.

Essentially, a correct zkVM can allow all its applications to relatively easily use zero-knowledge proofs in every transaction. A true zkVM is built according to the ZK priority principle and integrates it into every part of the technical stack.

Ethereum is originally a completely public and transparent blockchain. If developers now try to introduce privacy, its performance will definitely not be as good as a blockchain that supports privacy from the beginning.

From an engineering perspective, this is very difficult because developers have to code programs that were not designed to run on this type of field, leading to huge and more complex circuits.

Therefore, the performance of zkVM will be better than zkEVM, making it a very worthwhile technical solution.

Some projects using the zkVM solution have already emerged, such as L1: Aleo, Mina, etc.; L2: Aztec, etc. The market expectations for these projects are relatively high, but the cost-effectiveness of participation is not high. Below is an introduction to a zkVM project that is more suitable for investment.

Ola Network

Ola is a scalable ZKVM Rollup platform for privacy protection and compliance optimization, with its main features being programmable privacy, scalability, and multi-language compatibility. Ola aims to be a universal Layer2 scaling solution, providing privacy protection and scaling capabilities for various programmable Layer1 blockchains.

Ola recently received a $3 million investment in its seed round, led by Web3 Ventures and Foresight Ventures, with Token Metrics Ventures, J17 Capital, Skyland Ventures, LD Capital, and CatcherVC participating.

Ola's main products include the ZK-optimized virtual machine Ola-VM and the smart contract language Ola-lang.

Ola-lang is a general-purpose language developed based on ZK-VM, with higher programmability. Developers can flexibly deploy any type of smart contract using Ola-lang, whether on a public chain or an enterprise-level private chain.

The ZK-optimized virtual machine Ola-VM uses a reduced instruction set architecture and achieves better performance through complete ZK support and non-deterministic computation.

In short, Ola is building a Layer2 infrastructure with optional privacy and programmability.

It allows public chains to inherit network security and, by deploying corresponding verification contracts, obtain privacy protection and performance extension functions.

This approach avoids sacrificing the programmability and decentralization of public chains. Developers can add privacy and scaling solutions to different public chains as needed without making any on-chain changes.

This provides customizable privacy and scalability while maintaining the openness of public chains.

Currently, Ola has opened tasks in Ola Gala, where participants can qualify for the 2024 Ola Public Testnet and receive rewards such as NFTs.

Furthermore, on November 10th, Ola's official website opened applications for the Devnet testnet. Developers may pay attention to this application, as selected individuals can receive rewards, technical assistance, developer resources, and the opportunity to deploy Dapps on the Ola mainnet.

03. Fully Homomorphic Encryption (FHE)

Fully Homomorphic Encryption is a new technology applied to blockchain and is one of the public chain solutions that institutions are particularly keen on after the ZK craze. As a new concept, there are relatively few projects currently, and they are all in the early stages, making them very worthwhile for investment.

Fully Homomorphic Encryption (FHE) is a long-standing open problem in the field of cryptography. As early as 1978, Rivest, Adleman, and Dertouzos proposed this concept with the application background of banking.

Compared to general encryption schemes that focus on data storage security, the most interesting aspect of homomorphic encryption schemes is their focus on data processing security.

Specifically, homomorphic encryption provides a way to encrypt and process private data. In a homomorphic encryption scheme, other participants can process private data, but the processing does not reveal any original content. At the same time, users with the key can decrypt the processed data and obtain the correct result.

For example, if ALICE buys a gold bar and wants a worker to turn it into a necklace, is there a way for the worker to process the gold bar without obtaining any gold?

To solve this problem, ALICE can lock the gold bar in a sealed box with a single key. The box has two holes, each with a glove installed. The worker can wear the gloves to process the gold bar inside the box without stealing any gold.

After processing, ALICE retrieves the entire box, unlocks it, and obtains the processed necklace.

In this analogy, the box corresponds to the fully homomorphic encryption algorithm, and the worker's processing corresponds to the execution of operations with homomorphic properties, directly processing the encrypted result without obtaining the data.

Applications of Fully Homomorphic Encryption

In Web2, homomorphic encryption is almost tailor-made for cloud computing. Consider the following scenario: a user wants to process data but their computer's computing power is insufficient to obtain results in a timely manner. In this case, the user can use the concept of cloud computing to have the cloud help process the data and obtain results.

However, directly handing over the data to the cloud does not guarantee security. Therefore, the user can first use homomorphic encryption to encrypt the data, then have the cloud directly process the encrypted data, and return the processed results.

In this way, the user pays the cloud service provider and receives the processed results, while the cloud service provider earns fees. However, fully homomorphic encryption also has limitations due to computational power:

• High computational cost: Compared to traditional encryption, fully homomorphic encryption requires more complex mathematical algorithms and larger ciphertexts, making the speed of operations on encrypted data slower and more resource-intensive.

• Low computational efficiency: FHE (Fully Homomorphic Encryption) only supports arithmetic operations on encrypted data, such as addition, multiplication, and exponentiation. For more complex operations, such as sorting, searching, or string operations, more cumbersome processing is required before execution. It requires high computational power.

Fortunately, we are in an era of computational power explosion, and with the advancement of FHE and Web3 development, computational performance and costs are expected to match the requirements of FHE. Therefore, now is a good time to invest in the FHE track.

Fhenix

Fhenix is the first blockchain to adopt fully homomorphic encryption technology, providing encrypted data computation capabilities for EVM smart contracts.

The fhEVM used by Fhenix was initially developed by Zama, a cryptography company that builds open-source encryption solutions for blockchain and artificial intelligence, and was integrated with Fhenix Network after strategic cooperation.

In addition, Fhenix also uses Arbitrum's Nitro verifier and Zama's fully homomorphic ring-encrypted Rust library tfhe-rsr. This demonstrates the close relationship between Zama and Fhenix.

Zama's website shows that the company is providing FHE-based Web3 solutions for some cutting-edge Web2 use cases, such as facial recognition, voice recognition, and smart contracts (which is what Fhenix is currently doing). In the future, Zama is expected to integrate these applications into the Fhenix ecosystem.

In September of this year, Fhenix raised $7 million in a seed round of financing, led by Multicoin Capital and Collider Ventures, with participants including Node Capital, Bankless, HackVC, TaneLabs, Metaplanet, as well as Tarun Chitra and Robert Leshner's Robot Ventures.

Compared to zk, which can only verify encrypted data segments and cannot merge private data from multiple parties, and therefore cannot promote most encrypted computations, FHE allows a higher level of data security and supports unprecedented use cases through its "full" encryption capabilities.

Therefore, the ability to have privacy in Fhenix not only solves privacy issues but also paves the way for hundreds of new use cases—blind auctions, on-chain identity verification and KYC, tokenization of real-world assets, private voting for DAOs, and more.

Conclusion: Comparison of ZK and FHE

After understanding these two cutting-edge privacy smart contract solutions, many readers may still be confused about the technologies of Zero Knowledge Proof (ZK) and Fully Homomorphic Encryption (FHE).

In addition to the flexibility of encryption mentioned earlier, the difference between the two also lies in:

From a technical perspective, ZK focuses on proving correctness while protecting the privacy of statements; FHE focuses on performing calculations without decryption, protecting the privacy of data.

From the perspective of the blockchain industry, projects using ZK technology have developed earlier, from ZCash, which only had transfer functionality, to zkVM blockchains that support smart contracts currently under development, compared to FHE, which has more technical accumulation in the blockchain industry. FHE theory was born much later than ZK and has been a hot topic in academia, only recently appearing in Web3 projects that use FHE technology for financing, so its development is slower than ZK.

Both technologies point to development relying on computational power, and the development of the privacy track benefits from the computational power explosion. It is only thanks to the increase in computational power in recent years that these cutting-edge technologies can truly be accessible to users.

References

[01] Beyond ZK: The Definitive Guide to Web3 Privacy (Part 2)

https://scrt.network/blog/beyond-zk-guide-to-web3-privacy-part-2/

[02] Introduction to FHE: What is FHE, how does FHE work, how is it connected to ZK and MPC, what are the FHE use cases in and outside of the blockchain, etc.

https://taiko.mirror.xyz/2O9rJeB-1PalQeYQlZkn4vgRNr_PgzaO8TWUOM5wf3M

[03] Ola: A ZKVM-based, High-performance and Privacy-focused Layer2 platform

https://ethresear.ch/t/ola-a-zkvm-based-high-performance-and-privacy-focusedlayer2-platform/15248

[04] FHE-Rollups: Scaling Confidential Smart Contracts On Ethereum And Beyond – Whitepaper

https://www.fhenix.io/fhe-rollups-scaling-confidential-smart-contracts-on-ethereum-and-beyond-whitepaper/

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