Privacy has long been a concern in the internet industry, and Web3 also sees privacy as a core requirement, which has led to the development and implementation of technologies such as Zero-Knowledge Proofs (ZKP) and Secure Multi-Party Computation (MPC). However, recently, Fully Homomorphic Encryption (FHE) has also begun to emerge in the market, potentially filling the gap in existing privacy technologies and introducing new applications.
Table of Contents
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Introduction to Fully Homomorphic Encryption (FHE)
Conceptual Application: Performing calculations 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 complementing ZKP and MPC
Privacy Applications in Web3
Implementation Project: Fhenix Network
Project Introduction
Brief Overview of Operations
Unlocking More Privacy Applications
Homomorphic Encryption (HE) is a cryptographic encryption technique that aims to enhance data computation security. In simple terms, when data is encrypted using HE functions, the encrypted data can be used for other computations without the need for decryption, thereby improving data computation security and privacy.
Based on the maturity of the technology and the differences in operations that can be performed, HE can be further classified into:
– Partially Homomorphic Encryption (PHE)
– Somewhat Homomorphic Encryption (SWHE)
– Fully Homomorphic Encryption (FHE)
FHE is relatively more mature and can perform more complex encrypted computations, making it commercially viable and an important focus technology in the blockchain industry.
FHE ensures that data remains encrypted throughout the transmission, computation, and return process, preserving data confidentiality. Unlike traditional methods, data encrypted using FHE does not need to be decrypted during the computation process. This allows telecom operators, cloud computing providers, and ad analytics providers to complete tasks without seeing the plaintext data. After completing the computation, the data (still in encrypted form) is returned to the client, who can then decrypt it to obtain the desired result.
FHE is beneficial for both third-party service providers and clients. For service providers, it reduces concerns about storing privacy data and allows them to charge for computations. 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 user.
FHE allows users to encrypt data using FHE functions, such as encrypting data x and data y using f to become f(x) and f(y), and then sending them to external parties.
External calculators can compute f(x) + f(y) to obtain f(x+y) and return it to the user. The user can decrypt the result using the decryption function g to obtain the result g(f(x+y)) = x+y.
Homomorphic encryption has already been used in various applications:
– French technology companies use FHE technology to assist hospitals in analyzing patients’ private data.
– The South Korean government uses FHE, MPC, and other privacy technologies for privacy questionnaire surveys.
– National Sun Yat-sen University utilizes homomorphic encryption in the development of a “Privacy-protected and Secure Data Warehousing System for Medical Data Mining” project to provide fast medical services and securely upload medical data to the cloud.
In the Web3 industry, how does Fully Homomorphic Encryption (FHE) differ from Zero-Knowledge Proofs (ZKP), Secure Multi-Party Computation (MPC), Trusted Execution Environments (TEE), and why is there a need to introduce a new technology? Will it lead to new technological competition?
ZKP, FHE, MPC, and TEE are complementary technologies with different use cases. Apart from competition, they provide opportunities for combined innovation:
– ZKP provides relatively stronger privacy guarantees because “unencrypted” data never leaves the user’s device. Without the owner’s permission, no one can perform any calculations on this data. However, this also results in a loss of composability. ZKP is more suitable for verifying computations rather than running privacy-oriented smart contracts.
– FHE offers stronger composability but weaker privacy. If FHE needs to be used on the blockchain, it still requires a few parties with decryption keys under verification or mechanisms to record transaction information on the chain. However, due to its composability and privacy characteristics, there is still demand for its application on the chain.
– MPC provides an intermediate position between the above two methods. 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. It is suitable for privacy calculations with limited identity permissions, such as wallet private key management.
– TEE provides decryption and computation of transactions in a secure environment and is relatively mature and efficient. However, it relies heavily on the security of the execution environment and is suitable for applications with lower requirements for decentralization.
Each of the above technologies has unique advantages. ZKP is suitable for verifying the authenticity of things, FHE is suitable for applications that need to submit private data to contracts, MPC is suitable for privacy calculations with limited identity permissions, and TEE is suitable for applications with high-frequency computations 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 fund amount meets high net worth standards while using FHE to create asset change tables for the user 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, thereby increasing the feasibility of applications with high privacy requirements.
Token transactions:
Encrypting transaction contents can enhance user privacy and reduce MEV losses.
DAO voting:
Anonymous voting or selective disclosure at specific times can reduce 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.
To combine blockchain with Fully Homomorphic Encryption, apart from having tools for encrypting transactions when users sign them, there is also a need for smart contracts and virtual machines capable of quickly reading Fully Homomorphic Encryption functions. Finally, the challenge lies in how to enable nodes to verify transaction contents.
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 within the Ethereum ecosystem. It aims to address the transparency issues of Ethereum and other EVM networks by introducing privacy features to stimulate 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. The choice of Optimistic Rollups is because the technology is currently easier to implement, allowing for the rapid launch of FHE Layer2 for market testing.
Through the architecture of Arbitrum Nitro, Fhenix Network uses WebAssembly virtual machines (WASM) for fraud proofs and FHE logic compilation, running securely on WASM instead of EVM.
The core FHE logic of Fhenix Network is located in the fheOS code repository, which includes the packages developers need to implement FHE in smart contracts, such as TFHE-rs (developed by partner Zama).
The crucial 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 newly developed technology, but with advancements in technology, it is gradually being seen as a potential privacy protection solution. It complements 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.