Crypto Assets Discussion Heat and Price Dynamics Analysis

As of recent data statistics, three major Crypto Assets show:

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

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

The discussion heat for TON 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 decline of 0.25% compared to the previous week.

The Potential and Challenges of Homomorphic Encryption Technology

Homomorphic encryption ( FHE ), as a cutting-edge technology in the field of cryptography, has the core advantage of being able to perform computations directly on encrypted data without the need for a decryption process. This feature provides strong support for privacy protection and data processing. The application range of FHE is extensive, covering various fields such as finance, healthcare, cloud computing, machine learning, voting systems, the Internet of Things, and blockchain privacy protection. Nevertheless, FHE still faces many challenges on the path to commercialization.

Applications and Advantages of FHE

The greatest advantage of FHE 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 an important 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 decryption. This mechanism protects data privacy while achieving the required computational 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 focal point of concern. FHE can provide multi-party computing protection in these fields, enabling 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 Encryption Technologies

In the Web3 space, 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 various operations on encrypted data without needing to decrypt the data first. MPC allows parties to compute under encrypted data without sharing private information with each other. TEE provides computation in a secure environment, but has relatively limited flexibility in data processing.

These encryption technologies each have their advantages, but FHE stands out particularly in supporting complex computational 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 substantial computing resources, and its overhead significantly increases 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 FHE can perform addition and multiplication on encrypted data, it has limited support for complex nonlinear operations, which poses a bottleneck for AI applications involving deep neural networks. Currently, FHE schemes are mainly suitable for linear and simple polynomial calculations, and nonlinear model applications are significantly constrained.

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 research has proposed multi-key FHE frameworks that allow encrypted datasets with different keys to be operated on, the complexity of key management and system architecture significantly increases.

The Combination of FHE and Artificial Intelligence

In the data-driven era, AI is widely applied in multiple 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 the cloud computing scenario, FHE allows user data to be processed while remaining 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 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.

The Application of FHE in Blockchain

The application of FHE in blockchain mainly focuses on protecting data privacy, including on-chain privacy, AI training data privacy, on-chain voting privacy, and on-chain privacy transaction audits, among other areas. Currently, multiple projects are leveraging FHE technology to promote the realization of privacy protection:

• The FHE solution built by a certain project is widely used in multiple privacy protection projects. This project is based on TFHE technology, focusing on Boolean operations and low-width integer operations, and has developed an FHE development stack aimed at blockchain and AI applications.

• Another project developed a new smart contract language and HyperghraphFHE library, suitable for blockchain networks.

• Some projects utilize FHE for privacy protection in AI computing networks, supporting various AI models.

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

• There are also projects 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 capable of performing computations on encrypted data, has significant advantages in protecting data privacy. Although the commercial application 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 computing. In the future, FHE is anticipated to become the core technology supporting privacy-preserving computation, bringing revolutionary breakthroughs in data security.