What is Fully Homomorphic Encryption (FHE) and How Does Privacy Computing Transform the Blockchain Application Ecosystem?
Privacy has long been a concern in the internet industry, and Web3 considers privacy to be a basic requirement, which has led to the development of technologies such as Zero-Knowledge Proofs (ZKP) and Secure Multi-Party Computation (MPC). However, Fully Homomorphic Encryption (FHE) has recently emerged and may have the potential to fill the gaps in existing privacy technologies and create new applications.
Table of Contents:
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Introduction to Fully Homomorphic Encryption (FHE)
Concept: Performing computations directly 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
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 encrypted data can be used for other computations without the need for decryption. This improves 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)
Fully Homomorphic Encryption is a relatively mature technology that can perform more complex encrypted computations and is commercially viable. Therefore, it has become a key technology of interest in the blockchain industry.
FHE ensures that data remains encrypted throughout the transmission, computation, and return processes, protecting data confidentiality. Unlike traditional methods, data encrypted using FHE does not need to be decrypted during the computation process. This ensures that telecommunications providers, cloud computing providers, and advertising analysis providers can complete tasks without seeing the plaintext data. The computed data (still in encrypted form) can be returned to the customer, who can decrypt it to obtain the desired result.
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 computation fees. For users, it enhances data security and privacy.
Data encrypted using FHE can be analyzed or processed by third-party analysts while remaining in an encrypted state. The results can only be decrypted by the users themselves.
Fully Homomorphic Encryption allows users to encrypt data using FHE functions. For example, data x and data y can be encrypted using function f to become f(x) and f(y), which can then be sent to external parties.
External calculators can compute f(x) + f(y) to get f(x+y) and return f(x+y) to the user. The user can use the decryption function g to obtain the result g(f(x+y)) = x+y.
In this process, the external party does not know the plaintext data, but can still perform computations and submit the results to the data owner.
Homomorphic Encryption has already been used in many applications:
– A French technology company uses FHE technology to assist hospitals in analyzing patient privacy data.
– The South 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.
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 open up 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. No one can perform any calculations on this data without the data owner’s permission. However, on the other hand, it loses composability. It is more suitable for verifying calculations rather than running privacy-oriented smart contracts.
– FHE provides strong composability but weaker privacy. If FHE needs to be used on the blockchain, it still requires a few parties under verification or mechanism to have the decryption keys in order to record transaction information on the chain. However, due to its composability and privacy characteristics, it still has certain demand for on-chain applications.
– MPC provides an intermediate position between the two methods mentioned above. MPC completes the output without revealing the input, allowing computation (input) on privacy data. It provides more composability than ZKP but is limited to a small number of participants who can execute the computation. It is suitable for wallet private key management.
– TEE provides decryption and computation within a secure environment, and the technology is relatively mature and efficient. However, it relies too much on the security of the execution environment and is suitable for applications with lower requirements for decentralization.
Each of these technologies has its unique advantages. ZKP is suitable for verifying the authenticity of things, FHE is suitable for applications that require submitting sensitive data to contracts for computation, MPC is suitable for privacy calculations with restricted identities, and TEE is suitable for applications with high-frequency calculations and lower security requirements.
In the future, we can expect the emergence of products that combine multiple encryption technologies 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 good complementary technology that strengthens the privacy shortcomings of blockchain. FHE allows smart contracts to process ciphertext without the need to know the actual data, increasing the feasibility of applications with high privacy requirements.
Token transactions: By encrypting transaction contents, it enhances user privacy and reduces MEV losses.
DAO voting: It can enable anonymous voting or specific time-point public voting, reducing additional interference caused by public information.
Auctions: Only the final highest bid is disclosed, reducing the disclosure of bidding strategies.
Full-chain games: By hiding transaction information and opponent player strategies, it creates a more realistic information asymmetry game.
If we want to combine blockchain with Fully Homomorphic Encryption, besides the need for tools to encrypt when users sign transactions, we also need smart contracts and virtual machines that can quickly read Fully Homomorphic Encryption functions. Finally, we need to overcome how to validate transaction content by nodes.
The current solution is to build a virtual machine with native Fully Homomorphic Encryption operations. Fhenix Network claims to be an integrated network of FHE for the decentralized network. It aims to address the transparency issues of Ethereum and other EVM networks, and introducing privacy features to promote wider applications.
Fhenix Network is an FHE Rollup in the Ethereum ecosystem, built on Arbitrum Nitro fraud proofs. It provides modular FHE functionality while supporting EVM compatibility. Optimistic Rollups were chosen because they are currently easier to implement, allowing for the rapid launch of FHE Layer2 for market testing.
Using the architecture of Arbitrum Nitro, Fhenix Network uses WebAssembly (WASM) as the virtual machine for fraud proofs and FHE logic compilation. This allows for secure execution on WASM instead of the EVM.
The core FHE logic of Fhenix Network is located in the fheOS codebase, which includes the packages developers need to implement FHE in smart contracts, such as TFHE-rs (developed by partner Zama).
The most important decryption aspect 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 advancements in technology, it is gradually being seen as a potential solution for privacy protection. It fills the gaps in existing encryption technologies such as ZKP and MPC and has potential new applications, including privacy voting, full-chain games, and anti-MEV transfers. We can expect to see more interesting applications in the future.
FHE
Fhenix Network
MPC
TEE
ZKP
Further reading:
How will privacy encryption Layer2 project Manta Pacific break through zk Rollups as a latecomer?
Ankr introduces privacy-protected DID verification tool Ankr Verify
