Crypto Assets Market Weekly Report and Homomorphic Encryption Technical Analysis

As of October 13, the data statistics for major Crypto Assets are as follows:

The discussion volume of Bitcoin last week was 12.52K times, a decrease of 0.98% compared to the previous week. Last Sunday, the price was $63916, an increase of 1.62% compared to the previous Sunday.

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

The discussion volume for TON last week was 782 times, 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 an important technology in the field of cryptography that allows computations to be performed directly on encrypted data without the need for decryption. This feature has tremendous potential in protecting privacy and data processing and can be widely applied in fields such as finance, healthcare, cloud computing, machine learning, voting systems, the Internet of Things, and blockchain. Despite the broad application prospects, the commercialization of FHE still faces many challenges.

Advantages and Application Scenarios of FHE

The core advantage of FHE lies in privacy protection. For example, when one company needs to leverage the computational capabilities of another company to analyze data, FHE allows the data to be processed in an encrypted state, safeguarding data privacy while also completing the necessary 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, FHE plays a key role in multi-party computation protection, enabling parties to collaborate without exposing private information. In blockchain technology, FHE enhances the transparency and security of data processing by providing 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 all major privacy protection methods. FHE can perform various operations on encrypted data without needing to decrypt it first. MPC allows parties to compute under the condition of data encryption without sharing private information. TEE provides computation in a secure environment, but the flexibility of 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 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 a significant amount of computational resources, and its computational overhead increases significantly compared to unencrypted computations. For high-degree polynomial operations, the processing time grows polynomially, making it difficult to meet real-time computing requirements.

2. Limited operational capabilities: FHE can perform addition and multiplication on encrypted data, but support for complex nonlinear operations is limited, 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 it involves multi-user datasets.

The Combination of FHE and Artificial Intelligence

In the era of data-driven technology, AI is widely applied in multiple fields, but data privacy issues restrict the sharing of sensitive data. FHE provides a privacy protection solution for AI, allowing processing while keeping data in an encrypted state to ensure 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.

Applications and Projects of FHE in Blockchain

FHE is mainly used 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 others. Currently, multiple projects are leveraging FHE technology to promote the implementation of privacy protection:

• The FHE solution built by a certain project is widely used in multiple privacy protection projects.
• There are projects based on TFHE technology, focusing on Boolean operations and low-word-length integer operations, and have built an FHE development stack for blockchain and AI applications.
• Some projects have developed new smart contract languages and FHE libraries suitable for blockchain networks.
• There are also projects using FHE to achieve privacy protection in AI computing networks, supporting various AI models.
• Some projects combine FHE with artificial intelligence to provide a decentralized and privacy-preserving AI environment.
• As a Layer 2 solution for Ethereum, there are projects that support FHE Rollups and FHE Coprocessors, compatible with EVM and supporting smart contracts written in Solidity.

Conclusion

FHE, as an advanced technology that can perform calculations on encrypted data, has significant advantages in protecting data privacy. Although it 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 expected to become a core technology supporting privacy-preserving computation, bringing revolutionary breakthroughs to data security.