Crypto Assets Market Weekly Report and Homomorphic Encryption Technology Analysis

As of October 13, the data platform has compiled statistics on the discussion heat and price fluctuations of major Crypto Assets.

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

The discussion heat for Ethereum reached 3.63K last week, with a month-on-month increase of 3.45%. However, its price on Sunday was $2530, a decrease of 4% compared to the previous week.

The number of discussions on TON last week was 782, a decrease of 12.63% compared to the previous week. Its price on Sunday was $5.26, down 0.25% from the previous week.

Homomorphic Encryption(FHE) is a technology with broad prospects in the field of cryptography, allowing computations to be performed directly on encrypted data without the need for decryption. This feature provides strong support for privacy protection and data processing, and can be widely applied in various fields such as finance, healthcare, cloud computing, machine learning, voting systems, the Internet of Things, and blockchain privacy protection. Although the application prospects of FHE are broad, it still faces many challenges on the path to commercialization.

The Potential and Application Scenarios 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 other party to access the specific content, 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 decrypt it afterward to obtain the analysis results. This mechanism effectively protects data privacy while also enabling the computing party to complete the required work.

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 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 auditing functions.

Comparison of FHE and Other Encryption Methods

In the Web3 field, FHE, zero-knowledge proof (ZK), multi-party computation (MPC), and trusted execution environment (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 sharing private information with each other. TEE provides computation in a secure environment, but its flexibility in data processing is relatively limited.

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 often makes it inadequate for real-time applications.

Limitations and Challenges of FHE

Despite the strong theoretical foundation of FHE, it faces practical challenges in commercial applications:

1. High computational overhead: FHE requires a significant amount of computational resources, leading to a substantially increased overhead 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 is a bottleneck for artificial intelligence applications involving deep neural networks. Current FHE schemes are mainly suitable for linear and simple polynomial calculations, while 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 involving multi-user datasets. Although research has proposed multi-key FHE frameworks that allow operations on encrypted datasets with different keys, the key management and system architecture complexity significantly increase.

The Integration of FHE and Artificial Intelligence

In the current data-driven era, artificial intelligence (AI) is widely applied in multiple fields, but data privacy concerns 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 maintaining its encrypted state, ensuring privacy.

This advantage is particularly important under regulations such as GDPR, which 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.

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 private transaction auditing, among other areas. Currently, multiple projects are utilizing FHE technology to promote the realization of privacy protection.

Some projects based on FHE technology include:

• A solution focused on Boolean operations and low-word-length integer operations, constructing an FHE development stack for blockchain and AI applications.
• Developed a new 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 an Ethereum Layer 2 solution, it supports FHE Rollups and FHE Coprocessors, is compatible with EVM, 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 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. Moreover, with the development of blockchain technology, FHE will play an increasingly important role in privacy protection and secure computing. In the future, FHE could become the core technology supporting privacy-preserving computing, bringing new revolutionary breakthroughs in data security.