Crypto Assets Market Weekly Report and Homomorphic Encryption Technology Analysis

As of October 13, a data platform conducted a statistical analysis of the discussion frequency and price movements of major Crypto Assets:

The discussion frequency of Bitcoin last week was 12.52K, down 0.98% from the previous week, with a closing price on Sunday of 63916 USD, up 1.62% from last week.

Ethereum had 3.63K discussion mentions last week, an increase of 3.45% compared to the previous week. The closing price on Sunday was 2530 dollars, down 4% from last week.

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

Homomorphic Encryption (Fully Homomorphic Encryption, FHE) is a highly promising technology in the field of cryptography. Its core advantage lies in allowing direct computation on encrypted data without the need for decryption, providing strong support for privacy protection and data processing. FHE has broad application potential in various fields such as finance, healthcare, cloud computing, machine learning, voting systems, the Internet of Things, and blockchain privacy protection. However, despite its wide application prospects, FHE still faces many challenges on the path to commercialization.

Advantages and Application Scenarios of FHE

The biggest advantage of Homomorphic Encryption is its privacy protection. For example, when a company needs to utilize 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 analysis, and the calculation results remain encrypted. After decryption, the data owner can obtain the analysis results, thus protecting data privacy while completing the required computational tasks.

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. FHE can provide multi-party computation protection in these scenarios, enabling collaboration among parties 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 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 it first. MPC allows parties to compute while the data is encrypted, without sharing private information. TEE provides computation in a secure environment, but with 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 significant computational resources, with its computational overhead increasing significantly compared to unencrypted computation. For high-degree polynomial operations, processing time grows polynomially, making it difficult to meet real-time computation 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, support for complex nonlinear operations is limited, which is a bottleneck for artificial intelligence applications such as deep neural networks. Current FHE schemes are mainly suitable for linear and simple polynomial calculations, with significant limitations on the application of nonlinear models.

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. The multi-key FHE framework proposed in 2013 allows encrypted datasets with different keys to be operated on, but 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 applied 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 it is often in plaintext during processing. With FHE, user data can be processed while remaining in an encrypted state, ensuring data 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. The end-to-end encryption of FHE 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 auditing, among others. Currently, multiple projects are utilizing FHE technology to promote the realization of privacy protection:

1. A technology built by a certain FHE solution provider is widely used in multiple privacy protection projects.

2. A certain project is based on TFHE technology, focusing on Boolean operations and low bit-length integer operations, and has built an FHE development stack for blockchain and AI applications.

3. A project has developed a new smart contract language and the HyperghraphFHE library, suitable for blockchain networks.

4. A certain project utilizes FHE to achieve privacy protection in AI computing networks, supporting various AI models.

5. Another project combines FHE with artificial intelligence to provide a decentralized and privacy-preserving AI environment.

6. There are also projects serving 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 that can perform computations 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. Furthermore, 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 the core technology supporting privacy-preserving computation, bringing new revolutionary breakthroughs to data security.