The Potential and Challenges of FHE Homomorphic Encryption Technology

As of October 13, a data platform's statistics on major cryptocurrencies show:

The discussion volume for Bitcoin last week was 12.52K times, a decrease of 0.98% compared to the previous week. The closing price on Sunday was $63916, an increase of 1.62% compared to the previous week.

The discussion volume on Ethereum last week was 3.63K, a month-on-month increase of 3.45%. The closing price on Sunday was 2530 dollars, a month-on-month decrease of 4%.

The discussion volume for TON last week was 782 times, a decrease of 12.63% month-on-month. The closing price on Sunday was $5.26, down 0.25% month-on-month.

Homomorphic Encryption ( FHE ), as a rising star in the field of encryption, has its core advantage in the ability to perform operations directly on encrypted data without the need for decryption. This feature gives it broad application prospects in privacy protection and data processing, covering multiple fields such as finance, healthcare, cloud computing, machine learning, voting systems, the Internet of Things, and blockchain. However, despite the enormous potential of FHE, its commercialization still faces many challenges.

The Application Scenarios and Advantages of FHE

The main feature of FHE is its powerful privacy protection capability. For example, when a company needs to utilize another company's computing resources to analyze data but does not want to expose the content of the data, FHE can come into play. The data owner can transmit the encrypted data to the computing party for processing, and the computation results remain encrypted. The data owner can then decrypt the results to obtain the analysis. This mechanism not only protects data privacy but also achieves the required computing tasks.

For data-sensitive industries such as finance and healthcare, the privacy protection mechanism of FHE is particularly important. With the development of cloud computing and artificial intelligence, data security has increasingly become a focus of attention. FHE can achieve secure multi-party computation in these scenarios, allowing parties to collaborate without disclosing confidential information. In blockchain technology, FHE enhances the transparency and security of data processing by providing on-chain privacy protection and privacy transaction auditing functions.

Comparison of FHE and Other Privacy Protection Technologies

In the Web3 field, FHE is ranked alongside Zero-Knowledge Proofs ( ZK ), Multi-Party Computation ( MPC ), and Trusted Execution Environments ( TEE ) as the main privacy protection methods. Unlike ZK, FHE can perform various operations on encrypted data without the need to decrypt the data first. MPC allows parties to compute while the data remains in an encrypted state, without sharing private information. TEE provides computation in a secure environment, but has relatively limited flexibility in data processing.

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

Challenges Facing FHE

Although the theoretical foundation of FHE is strong, it has encountered practical difficulties in the commercialization process:

1. Large-scale computational overhead: FHE requires a significant amount of computational resources, and its overhead increases significantly compared to unencrypted computation. 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, which in turn increases deployment complexity.

2. Limited operational capability: While FHE can perform addition and multiplication on encrypted data, its support for complex nonlinear operations is limited, which poses a bottleneck for AI applications involving deep neural networks. Current FHE schemes are primarily suitable for linear and simple polynomial computations, with significant restrictions on the application of nonlinear models.

3. Multi-user support complexity: FHE performs well in single-user scenarios, but the system complexity rises sharply when dealing with multi-user datasets. The multi-key FHE framework allows operations on encrypted datasets with different keys, but the complexity of key management and system architecture increases significantly.

The Combination of FHE and Artificial Intelligence

In the data-driven era, AI is widely used in various fields, but concerns about data privacy often make users reluctant to share sensitive information. FHE provides a privacy-preserving solution for AI. In the cloud computing scenario, FHE allows user data to be processed in an encrypted state, ensuring privacy.

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

The Application 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 review, among other areas. Currently, multiple projects are utilizing FHE technology to promote privacy protection.

• A technology built by a certain FHE solution provider is widely used in multiple privacy protection projects.
• There are projects based on TFHE technology, focusing on Boolean operations and low-word-length integer operations, and building an FHE development stack for blockchain and AI applications.
• Some projects have developed new smart contract languages and FHE libraries suitable for blockchain networks.
• There are projects utilizing FHE to achieve privacy protection in AI computing networks, supporting various AI models.
• Some projects combine FHE with artificial intelligence to provide a decentralized and privacy-preserving AI environment.
• As an Ethereum Layer 2 solution, the project 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 commercialization of FHE 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 computing. In the future, FHE may become a core technology supporting privacy-preserving computation, bringing revolutionary breakthroughs to data security.