The Potential and Challenges of FHE Technology in Privacy Protection and Blockchain Applications

Fully Homomorphic Encryption (FHE) is a promising technology in the field of cryptography, with its core advantage being the ability to perform computations directly on encrypted data without the need for decryption. This feature provides strong support for privacy protection and data processing. FHE has a broad application prospect in various fields such as finance, healthcare, cloud computing, machine learning, voting systems, the Internet of Things, and blockchain privacy protection. However, despite the enormous potential of FHE, its commercialization path still faces numerous challenges.

Advantages and Applications of FHE

The most significant advantage of FHE is privacy protection. For example, when a company needs to utilize another company's computing power to analyze data but does not want the other party to access the content of the data, FHE can play a crucial role. The data owner can transmit the encrypted data to the computing party for analysis, and the computation results remain in an encrypted state, allowing the data owner to decrypt and obtain the analysis results. This mechanism effectively protects data privacy while also enabling the computing party to complete the required work.

For industries with high data sensitivity, such as finance and healthcare, this privacy protection mechanism is particularly important. With the rapid 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 private information. In Blockchain technology, FHE enhances the transparency and security of data processing through on-chain privacy protection and privacy transaction review functions.

Comparison of FHE and Other Cryptographic Technologies

In the Web3 domain, FHE, zero-knowledge proofs (ZK), multi-party computation (MPC), and trusted execution environments (TEE) are the main privacy protection methods. Unlike ZK, FHE can perform multiple operations on encrypted data without needing to decrypt it first. MPC allows parties to compute while the data is encrypted, without sharing private information. TEE provides computation in a secure environment, but has relatively limited flexibility in data processing.

These cryptographic technologies each have their advantages, but FHE stands out particularly in supporting complex computing tasks. However, FHE still faces high computational overhead and poor scalability issues 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. Large-scale computational overhead: FHE requires a significant amount of computational resources, and its overhead increases significantly compared to unencrypted computations. For high-degree polynomial operations, the processing time grows polynomially, making it difficult to meet real-time computing demands. Reducing costs relies on dedicated hardware acceleration, but this also increases deployment complexity.

2. Limited operational capability: Although Fully Homomorphic Encryption (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 primarily suitable for linear and simple polynomial calculations, with significant limitations on the application of nonlinear models.

3. Complexity of multi-user support: FHE performs well in single-user scenarios, but the system complexity increases dramatically when dealing with multi-user datasets. Although there are multi-key FHE frameworks that allow encrypted datasets with different keys to be processed, 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 is widely applied in various fields, but concerns about data privacy often make users reluctant to share sensitive information. FHE provides a privacy protection solution for the AI field. In cloud computing scenarios, data is usually encrypted during transmission and storage, but is often in plaintext during processing. With FHE, user data can be processed while remaining encrypted, ensuring data privacy.

This advantage is particularly important under regulations such as GDPR, as these regulations require users to have the right to be informed about how their data is processed and ensure that data is protected during transmission. The end-to-end encryption of FHE provides assurance for compliance and data security.

The Application of FHE in Blockchain and Related Projects

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:

• An FHE solution built on TFHE technology, focusing on Boolean operations and low-bit-length integer operations, and has constructed an FHE development stack for Blockchain and AI applications.

• Developed a new type of smart contract language and HyperghraphFHE library, suitable for Blockchain networks.

• Utilize FHE to achieve privacy protection in AI computing networks, supporting various AI models.

• Combine FHE with artificial intelligence to provide a decentralized and privacy-preserving AI environment.

• As a Layer 2 solution for Ethereum, it supports FHE Rollups and FHE Coprocessors, is EVM compatible, and supports 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 commercial applications of FHE face 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 computing. In the future, FHE may become a core technology supporting privacy-preserving computation, bringing revolutionary breakthroughs in data security.