Encryption Asset Market Dynamics and the Potential of Homomorphic Encryption Technology

As of October 13, the discussion heat and price performance of major encryption assets are as follows:

A certain data platform's statistics show that the discussion volume of Bitcoin last week was 12.52K times, a decrease of 0.98% compared to the previous week. Its closing price on Sunday was 63916 dollars, an increase of 1.62% compared to the previous week.

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

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

Homomorphic Encryption ( FHE ), as a cutting-edge technology in the field of encryption, has broad application prospects. Its core advantage lies in the ability to perform calculations directly on encrypted data without the need for decryption, providing strong support for privacy protection and data processing. FHE can be applied in various fields including finance, healthcare, cloud computing, machine learning, voting systems, the Internet of Things, and blockchain privacy protection. Nevertheless, the commercialization of FHE still faces some challenges.

The Potential and Application Scenarios of FHE

The greatest advantage of FHE is its ability to protect data privacy. For example, when a company needs to leverage the computational power of another company to analyze data but does not want the content of the data to be known by the other party, FHE can play a role. The data owner can transmit the encrypted data to the computing party for processing, and the computation results remain encrypted. The data owner can obtain the analysis results after decrypting. This mechanism not only protects data privacy but also does not impede necessary computational work.

For data-sensitive industries such as finance and healthcare, the privacy protection capabilities of FHE are particularly important. With the development of cloud computing and AI, data security has increasingly become a focal point of concern. FHE can provide privacy protection in multi-party computation, enabling parties to collaborate without disclosing sensitive information. In blockchain technology, FHE enhances the transparency and security of data processing through on-chain privacy protection and privacy transaction auditing.

Comparison of FHE and Other Encryption Technologies

In the Web3 field, FHE, Zero-Knowledge Proofs ( ZK ), Multi-Party Computation ( MPC ), and Trusted Execution Environment ( TEE ) are all major privacy protection methods. Unlike ZK, FHE can perform multiple operations on encrypted data without needing to decrypt first. MPC allows multiple parties to compute while keeping data encrypted, without sharing private information. TEE provides a secure computing environment, but is relatively limited in terms of data processing flexibility.

These technologies each have their advantages, but 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.

Limitations and Challenges of FHE

Although the theoretical foundation of FHE is strong, there have been some practical challenges in the commercialization process:

1. Large-scale computational overhead: FHE requires substantial computational resources, and its overhead significantly increases compared to unencrypted computation. For high-degree polynomial operations, processing time grows polynomially, making it difficult to meet real-time computing needs. Although costs can be reduced through dedicated hardware acceleration, this also increases the complexity of deployment.

2. Limited operational capabilities: Although 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 mainly suitable for linear and simple polynomial computations, 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 increases dramatically when dealing with multi-user datasets. Although some studies have proposed multi-key FHE frameworks that allow operations on encrypted datasets with different keys, the key management and system architecture complexity significantly increase.

The Combination of FHE and AI

In the current data-driven era, AI is widely used in various fields, but concerns about data privacy often deter users from sharing sensitive information. FHE provides a privacy protection solution for the AI field. In cloud computing scenarios, FHE allows user data to be processed while remaining in an encrypted state, 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. FHE's end-to-end encryption provides assurance for compliance and data security.

Application of FHE in Blockchain

FHE is primarily 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. Currently, multiple projects are leveraging FHE technology to promote the realization of privacy protection:

• The FHE solution built by a certain company is widely used in multiple privacy protection projects. The company focuses on Boolean operations and low-word-length integer operations based on TFHE technology and has developed an FHE development stack for blockchain and AI applications.

• Another company has developed a new smart contract language and FHE library for blockchain networks.

• There are also projects using FHE to achieve privacy protection in AI computing networks, supporting various AI models.

• A certain network combines FHE and AI to provide a decentralized and privacy-preserving AI environment.

• As an Ethereum Layer 2 solution, a certain project supports FHE Rollups and FHE Coprocessors, is compatible with EVM, and supports smart contracts written in Solidity.

Conclusion

FHE, as an advanced technology that enables computation on encrypted data, has significant advantages in protecting data privacy. Although the commercialization of FHE currently 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 is expected to become a core technology supporting privacy-preserving computation, bringing revolutionary breakthroughs to data security.