Homomorphic Encryption: The Future Star and Challenges of Web3 Privacy Protection

As of October 13, the discussion trends and price changes of the three main Crypto Assets are as follows:

The number of discussions about Bitcoin last week was 12.52K, slightly down 0.98% from the previous week. Its closing price on Sunday was $63,916, an increase of 1.62% compared to the same period last week.

The discussion heat of Ethereum increased last week, reaching 3.63K, an increase of 3.45%. However, its closing price on Sunday was $2530, a decrease of 4% compared to the same period last week.

The discussion count for another Crypto Asset last week was 782 times, a decrease of 12.63% compared to the previous week. Its closing price on Sunday was $5.26, a slight decrease of 0.25% compared to the previous week.

Fully Homomorphic Encryption (FHE) is a highly promising technology in the field of cryptography. Its core advantage lies in the ability to perform computations directly on encrypted data without the need for decryption, providing strong support for privacy protection and data processing. FHE has a wide range of applications, encompassing finance, healthcare, cloud computing, machine learning, voting systems, the Internet of Things, and blockchain privacy protection among others. Nevertheless, FHE still faces numerous challenges on the road to commercialization.

The Potential and Application Scenarios of FHE

The biggest advantage of homomorphic encryption lies in privacy protection. For example, when a company needs to utilize the computing power of another company to analyze data, but does not want the latter to access the specific content, FHE can play a role. The data owner can encrypt the information and transmit it to the computing party for processing, while the computation results remain in an encrypted state; the data owner can obtain the analysis results after decrypting. This mechanism protects data privacy while achieving the required computational work.

This privacy protection mechanism is especially important for data-sensitive industries such as finance and healthcare. With the development of cloud computing and artificial intelligence, data security has increasingly become a focal point of concern. FHE can provide multi-party computation protection in these scenarios, allowing all parties to collaborate without exposing confidential information. In blockchain technology, FHE enhances the transparency and security of data processing through on-chain privacy protection and privacy transaction auditing functions.

Comparison of FHE and Other Encryption Methods

In the Web3 domain, FHE, Zero-Knowledge Proofs (ZK), Multi-Party Computation (MPC), and Trusted Execution Environments (TEE) are all major privacy protection methods. Unlike ZK, FHE can perform various operations on encrypted data without needing to decrypt it first. MPC allows parties to compute in an encrypted data environment without sharing private information with each other. TEE, on the other hand, provides computation in a secure environment but has relatively limited flexibility in data processing.

These encryption technologies each have their advantages, but in supporting complex computational tasks, FHE stands out particularly. However, FHE still faces high computational overhead and poor scalability in practical applications, which limits its performance in real-time applications.

Limitations and Challenges of FHE

Despite the strong theoretical foundation of FHE, it faces practical challenges in commercial applications:

1. High Computational Overhead: FHE requires a significant amount of computational resources, with costs markedly increasing compared to unencrypted computations. For high-degree polynomial operations, the processing time grows polynomially, making it difficult to meet real-time computing demands. Cost reduction relies on dedicated hardware acceleration, which also increases deployment complexity.

2. Limited operational capacity: Although FHE can perform addition and multiplication on encrypted data, it has limited support for complex nonlinear operations, which is a bottleneck for artificial intelligence applications involving deep neural networks. Current FHE schemes are mainly suitable for linear and simple polynomial calculations, and the application of nonlinear models is significantly restricted.

3. Complexity of Multi-User Support: FHE performs well in single-user scenarios, but the system complexity rises sharply when dealing with multi-user datasets. Although there are multi-key FHE frameworks that allow operations on encrypted datasets with different keys, the complexity of key management and system architecture increases significantly.

The Combination of FHE and Artificial Intelligence

In the current data-driven era, artificial intelligence (AI) is widely used in multiple fields, but concerns about data privacy often make users reluctant to share sensitive information. FHE provides privacy protection solutions for the AI field. In cloud computing scenarios, data transmission and storage are often encrypted, but the processing stage is usually in plaintext. With FHE, user data can be processed while remaining in an encrypted state, ensuring privacy.

This advantage is particularly important under regulations such as GDPR, which require users to have the right to know how their data is processed and ensure that data is protected during transmission. FHE's end-to-end encryption provides assurance for compliance and data security.

Current Applications and Projects of FHE in Blockchain

FHE is mainly used in blockchain to protect data privacy, including on-chain privacy, AI training data privacy, on-chain voting privacy, and on-chain privacy transaction auditing. Currently, multiple projects are leveraging FHE technology to promote the realization of privacy protection:

1. The FHE solution developed by a certain company is widely used in multiple privacy protection projects.

2. A company based on TFHE technology focuses on Boolean operations and low-word-length integer operations, and has built an FHE development stack for blockchain and AI applications.

3. Another company developed a new smart contract language and HyperghraphFHE library for blockchain networks.

4. Some companies utilize FHE to achieve privacy protection in AI computing networks, supporting various AI models.

5. A certain project combines FHE and artificial intelligence to provide a decentralized and privacy-preserving AI environment.

6. There are also projects that serve as Layer 2 solutions for Ethereum, supporting FHE Rollups and FHE Coprocessors, compatible with EVM and supporting smart contracts written in Solidity.

Conclusion

FHE, as an advanced technology that can perform computations on encrypted data, has significant advantages in protecting data privacy. Although the current commercialization of FHE still faces challenges such as high computational overhead and poor scalability, these issues are expected to be gradually resolved through hardware acceleration and algorithm optimization. With the development of blockchain technology, FHE will play an increasingly important role in privacy protection and secure computation. In the future, FHE has the potential to become a core technology supporting privacy-preserving computing, bringing new revolutionary breakthroughs in data security.