What is Fully Homomorphic Encryption (FHE)? How does Privacy Computing Transform Blockchain Application Ecosystem?
Privacy has long been a concern in the internet industry, and Web3 considers privacy to be a basic need, which has prompted the development of technologies such as Zero-knowledge Proofs (ZKP) and Secure Multi-Party Computation (MPC). However, Fully Homomorphic Encryption (FHE) technology has also gradually emerged in the market recently, which may have the opportunity to fill the gaps in existing privacy technologies and bring new applications.
Table of Contents:
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Introduction to Fully Homomorphic Encryption (FHE)
Concept: Performing calculations on encrypted data without decryption
Algebraic concept: f(x) + f(y) = f(x+y)
Case study
The importance of Fully Homomorphic Encryption in Web3
FHE complements ZKP and MPC
Privacy applications in Web3
Implementation project: Fhenix Network
Project introduction
Brief overview of operation principles
Unlocking more privacy applications
Homomorphic Encryption (HE) is a cryptographic encryption technology that aims to enhance data computation security. Simply put, when data is encrypted using HE functions, the data can be processed without the need for decryption, thereby enhancing data computation security and privacy.
Based on the maturity of the technology and the differences in operations that can be performed, it can be further divided into:
– Partially Homomorphic Encryption (PHE)
– Somewhat Homomorphic Encryption (SWHE)
– Fully Homomorphic Encryption (FHE)
Among them, Fully Homomorphic Encryption technology is relatively mature and can perform more complex encrypted operations, making it commercially viable. Therefore, it has also become a key technology of interest in the blockchain industry.
FHE ensures that data remains encrypted throughout the transmission, computation, and return processes, protecting the confidentiality of the data. Unlike traditional methods, data encrypted using FHE does not need to be decrypted during the calculation process. This ensures that telecom operators, cloud computing providers, and advertising analysis companies can complete tasks without seeing plaintext, and return the computed data (still in encrypted form) to customers, who can then decrypt it to obtain the desired results.
FHE is beneficial for both third-party service providers and customers. For service providers, it reduces concerns about storing privacy data and allows them to charge for computation. For users, it enhances data security and privacy.
Data encrypted using FHE can be analyzed or processed by third parties while remaining encrypted, and the results can only be decrypted by the users themselves.
Homomorphic Encryption allows users to encrypt data using FHE functions, for example, encrypting data x and data y using f to become f(x) and f(y), and then sending them externally.
External calculators can perform calculations on f(x) + f(y) to obtain f(x+y), and return f(x+y) to the user. The user can decrypt the result using the decryption function g to get x+y.
During this process, the external party does not know the plaintext data but can still perform the calculation and submit it to the data owner.
In fact, Homomorphic Encryption has been used in many applications:
– French technology companies use FHE technology to assist hospitals in analyzing patient privacy data.
– The Korean government uses FHE, MPC, and other privacy technologies for privacy questionnaire surveys.
– National Sun Yat-sen University uses Homomorphic Encryption to develop a “Privacy-protected and Secure Data Mining Medical Data Warehouse System” project, enabling the secure uploading of medical data to the cloud for the development of efficient medical services.
What are the differences between Zero-knowledge Proofs (ZKP), Fully Homomorphic Encryption (FHE), Secure Multi-Party Computation (MPC), and Trusted Execution Environments (TEE) in the Web3 industry? Why is there a need to introduce a new technology? Will it create new technological competition?
ZKP, FHE, MPC, and TEE are complementary technologies and are used in different scenarios. In addition to competition, they bring more opportunities for combination and innovation:
ZKP provides relatively strong privacy guarantees because “unencrypted” data never leaves the user’s device. Without the data owner’s permission, no one can perform any calculations on this data. However, on the downside, it loses composability. It is more suitable for verifying computations rather than running privacy-preserving smart contracts.
FHE has stronger composability but weaker privacy. If FHE needs to be used on the blockchain, it still requires a few parties under verification or mechanisms to possess the decryption key to record transaction information on the chain. However, due to its composability and privacy features, there is still a demand for its application on the chain.
MPC provides an intermediate position between the above two methods. MPC completes the output without revealing the inputs, allowing computation on privacy data. It offers more composability than ZKP but is limited to a small number of participants. It is suitable for privacy calculations with limited identity permissions, such as wallet private key management.
TEE provides secure decryption and computation of transactions in a secure environment. The technology is relatively mature and efficient, but it relies heavily on the security of the execution environment. It is suitable for applications with lower decentralization requirements.
Each of the above technologies has unique advantages. ZKP is suitable for verifying the authenticity of things, FHE is suitable for applications that require submitting privacy data to contracts for computations, MPC is suitable for privacy calculations with restricted identity permissions, and TEE is suitable for applications with high-frequency computations and lower security requirements.
In the future, it is foreseeable that products combining multiple encryption technologies will emerge to meet various functional requirements.
For example, asset management tools can use ZKP to verify whether a user’s funds meet high net worth standards, while using FHE to create asset change tables for users without transmitting individual asset data.
For the blockchain industry, Fully Homomorphic Encryption is also a complementary technology that strengthens the privacy shortcomings of blockchain. FHE allows smart contracts to process ciphertext without knowing the actual data, increasing the feasibility of applications with high privacy requirements.
Token transactions:
By encrypting transaction contents, user privacy can be enhanced while reducing MEV losses.
DAO voting:
It can achieve anonymous voting or public voting at specific time points, reducing additional interference caused by public information.
Auctions:
Only the final highest bid is disclosed, reducing the disclosure of buyer bidding strategies.
Full-chain games:
By hiding transaction information and opponent player strategies, a more realistic information asymmetry game can be created.
(See the article “How Full-Chain Games Bring True Asymmetric Information Games in Games”)
To combine blockchain with Fully Homomorphic Encryption, in addition to users needing tools to encrypt when signing transactions, there is also a need for smart contracts and virtual machines that can quickly read Fully Homomorphic Encryption functions. Finally, it is necessary to overcome how nodes can verify transaction contents.
The current solution is to create a virtual machine with native Fully Homomorphic Encryption operations. Fhenix Network claims to be an integrated decentralized network of FHE within the Ethereum ecosystem. It aims to address the transparency issues of Ethereum and other EVM networks by introducing privacy features to promote broader applications.
Fhenix Network is an FHE Rollup in the Ethereum ecosystem, built on Arbitrum Nitro fraud-proof, providing modular FHE functionality while supporting EVM compatibility. The choice of Optimistic Rollups is because the current technology is easier to implement, allowing for the rapid launch of FHE Layer2 for market testing.
Using the architecture of Arbitrum Nitro, Fhenix Network utilizes the WebAssembly virtual machine (WASM) for fraud-proof and FHE logic compilation to run securely on WASM instead of the EVM.
The core FHE logic of Fhenix Network is located in the fheOS codebase, which contains the packages developers need to implement FHE in smart contracts, such as TFHE-rs (constructed by partner Zama).
The crucial decryption part of Fully Homomorphic Encryption is handled by the Threshold Network (TSN) module in Fhenix Network. When data needs to be decrypted, TSN is responsible for decrypting and returning the data.
Fully Homomorphic Encryption is not a recently developed technology, but with technological advancements, it is gradually being seen as a potential solution for privacy protection in the encryption community. It fills the gaps in existing encryption technologies such as ZKP and MPC and has potential new applications, including privacy voting, full-chain games, anti-MEV transfers, etc. More interesting applications are expected to emerge in the future.
FHE
Fhenix Network
MPC
TEE
ZKP
Further reading:
Secure and user-friendly Web3 services! Bitget Wallet introduces MPC wallet
Coinbase launches Wallet as a Service, making it easy to create and integrate on-chain wallets
