Crypto Assets Market Weekly Report and Homomorphic Encryption Technical Analysis

As of October 13, the discussion heat and price performance of several 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 price on Sunday last week was 63916 dollars, an increase of 1.62% compared to the previous Sunday.

The discussion count for Ethereum last week was 3.63K, an increase of 3.45% from the previous week. The price last Sunday was 2530 USD, down 4% from the previous Sunday.

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

Homomorphic Encryption (FHE) is a cutting-edge technology in the field of cryptography that allows computations to be performed directly on encrypted data without needing to decrypt it. This characteristic gives FHE immense potential in privacy protection and data processing, making it applicable in various fields such as finance, healthcare, cloud computing, and machine learning. However, despite its broad application prospects, the commercialization of FHE still faces numerous challenges.

Advantages and Application Scenarios of FHE

The greatest advantage of FHE lies in privacy protection. For example, one company can send encrypted data to another company for analysis, which can perform calculations without knowing the original data content and return the encrypted results. This mechanism is particularly important for data-sensitive industries such as finance and healthcare.

In the fields of cloud computing and artificial intelligence, FHE can provide multi-party computation protection, 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 Proofs (ZK), Multi-Party Computation (MPC), and Trusted Execution Environments (TEE) are the main privacy protection methods. FHE can perform various operations on encrypted data without needing to decrypt it first, which is an advantage over ZK. MPC allows parties to compute while the data is encrypted, without having to share private information with each other. TEE provides computation in a secure environment but has relatively limited flexibility in data processing.

Although FHE performs excellently in supporting complex computational tasks, it 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, it faces practical challenges in commercial applications:

1. Large-scale computing overhead: FHE requires a significant amount of computing resources, and its computational overhead increases significantly compared to unencrypted computations. For high-degree polynomial operations, processing time grows polynomially, making it difficult to meet real-time computing demands.

2. Limited operational capability: FHE primarily supports addition and multiplication of encrypted data, with limited support for complex nonlinear operations, which is a bottleneck for artificial intelligence applications involving deep neural networks.

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 multi-key FHE frameworks have been proposed, the complexity of key management and system architecture is significantly increased.

The Combination of FHE and Artificial Intelligence

In the current data-driven era, FHE provides privacy protection solutions for the AI field. Through FHE, users' data can be processed while remaining in an encrypted state, ensuring the privacy of the data. 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.

The Application and Projects of FHE in Blockchain

FHE is mainly applied in blockchain to protect 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 realization of privacy protection:

• The FHE solution built by a certain company is widely used in multiple privacy protection projects.
• Some projects focus on Boolean operations and low-word-length integer operations, and have built an FHE development stack for blockchain and AI applications.
• A project has developed a new smart contract language and FHE library suitable for blockchain networks.
• Some projects utilize FHE to achieve privacy protection in AI computing networks, supporting various AI models.
• There are projects that combine FHE with artificial intelligence to provide a decentralized and privacy-preserving AI environment.
• Some projects serve 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 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. With the development of blockchain technology, FHE will play an increasingly important role in privacy protection and secure computing. In the future, FHE has the potential to become the core technology supporting privacy-preserving computation, bringing new revolutionary breakthroughs in data security.