Crypto Assets Discussion and Price Weekly Report

According to the latest data statistics, as of the 13th of this month, the discussion heat and price changes of major Crypto Assets are as follows:

The number of discussions about Bitcoin last week was 12.52K, a decrease of 0.98% compared to the previous week. The closing price on Sunday was 63916 USD, an increase of 1.62% compared to the previous week.

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

The Potential and Challenges of Homomorphic Encryption Technology

Homomorphic encryption ( FHE ) is a cutting-edge technology in the field of cryptography that allows for computations to be performed directly on encrypted data without the need for decryption. This feature provides strong support for privacy protection and data processing. FHE has broad application prospects 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 is still facing numerous challenges.

Advantages and Application Scenarios of FHE

The biggest advantage of FHE lies in privacy protection. For example, when a company needs to leverage another company's computing power to analyze data but does not want the other party to access the specific content, FHE can play a role. The data owner can transmit the encrypted data to the computing party for processing, while the computation results remain encrypted. The data owner can obtain the analysis results after decryption, which both protects data privacy and completes the required computing task.

This privacy protection mechanism is particularly 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 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 Methods

In the Web3 field, 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 while the data is encrypted, without needing to share private information with each other. TEE provides computation in a secure environment, but is relatively limited in terms of flexibility for data processing.

These encryption 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

Despite the strong theoretical foundation of FHE, there are some practical challenges encountered in commercial applications:

1. High computational overhead: FHE requires a significant amount of computational resources, with costs increasing substantially 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 specialized hardware acceleration, which 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. This poses a bottleneck for artificial intelligence applications involving deep neural networks. Currently, 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 research has proposed 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 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 in an encrypted state, ensuring privacy.

This advantage is especially 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

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 review, among other directions. Currently, multiple projects are utilizing FHE technology to promote the implementation of privacy protection.

Some projects built FHE solutions that are widely used in several blockchain privacy protection projects. These projects include:

• Based on TFHE technology, focusing on Boolean operations and low-word-length integer operations, and building a project for FHE development stack targeted at blockchain and AI applications.

• Developed a new smart contract language and HyperghraphFHE library, suitable for projects on blockchain networks.

• Implementing privacy protection in AI computing networks using FHE, supporting various AI model projects.

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

• As an Ethereum Layer 2 solution, it supports FHE Rollups and FHE Coprocessors, is compatible with EVM, and supports writing smart contracts 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. In addition, 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 computation, bringing revolutionary breakthroughs to data security.