Crypto Assets Market Weekly Report and Discussion on Homomorphic Encryption Technology

As of October 13, the discussion heat and price performance of the three major Crypto Assets are as follows:

The discussion frequency of 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.

Ethereum had 3.63K discussions last week, a week-on-week increase of 3.45%. The closing price on Sunday was $2530, a week-on-week decrease of 4%.

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

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 gives FHE great potential in privacy protection and data processing, with wide applications in finance, healthcare, cloud computing, machine learning, and many other fields. However, the commercialization of FHE still faces many challenges.

The Application Value of FHE

The biggest advantage of FHE lies in privacy protection. For example, a company can hand over encrypted data to another company for analysis without worrying about data leakage. This mechanism is particularly important in data-sensitive industries such as finance and healthcare. With the development of cloud computing and artificial intelligence, the application prospects of FHE in multi-party secure computing are broad. In the blockchain field, FHE can be used to enhance the privacy and security of transactions.

Comparison of FHE and Other Encryption Technologies

In the Web3 field, FHE, zero-knowledge proof ( ZK ), multi-party computation ( MPC ), and trusted execution environment ( TEE ) are all major privacy protection methods. In comparison, FHE can perform various operations on encrypted data without the need for decryption. MPC allows multiple parties to perform joint computations while protecting privacy. TEE provides a secure computing environment, but with lower flexibility.

Although FHE performs excellently in supporting complex computational tasks, its high computational overhead and scalability issues still limit its widespread use in real-time applications.

Challenges Facing FHE

1. High computational overhead: FHE requires a large amount of computational resources, especially for high-degree polynomial operations, where the processing time grows polynomially.

2. Limited operational capabilities: FHE mainly supports addition and multiplication operations, with limited support for complex nonlinear operations, which restricts its use in AI applications such as deep neural networks.

3. Multi-user support complexity: When dealing with multi-user datasets, the complexity of the FHE system increases significantly, making key management and architecture design more challenging.

The Combination of FHE and AI

In today's data-driven era, AI technology is widely applied in various fields. However, data privacy issues often hinder users from sharing sensitive information. Homomorphic Encryption provides a privacy-preserving solution for AI, allowing data to be processed in an encrypted state, ensuring privacy security. This feature is especially important under strict data protection regulations such as GDPR.

The Application of FHE in Blockchain

FHE is mainly used in the blockchain field to protect data privacy, including on-chain privacy, AI training data privacy, voting privacy, and transaction review. Currently, several projects are exploring the practical application of FHE technology:

• A certain project: Based on TFHE technology, focusing on Boolean operations and low-bit-length integer operations, to build an FHE development stack for blockchain and AI applications.

• A certain project: Developed a new smart contract language and FHE library suitable for blockchain networks.

• A certain project: Utilizing FHE to achieve privacy protection in AI computing networks, supporting various AI models.

• A certain project: Combining FHE and AI to provide a decentralized and privacy-preserving AI environment.

• Certain Project: As a Layer 2 solution for Ethereum, it supports FHE Rollups and FHE Coprocessors, is EVM compatible, and supports Solidity smart contracts.

Conclusion

FHE, as an advanced technology that can perform computations on encrypted data, has significant advantages in protecting data privacy. Although it still faces challenges such as computational overhead and scalability, these issues are expected to be addressed through hardware acceleration and algorithm optimization. With the development of blockchain technology, the importance of FHE in the fields of privacy protection and secure computing will become increasingly prominent, and it is expected to become a core technology supporting privacy-preserving computation, bringing revolutionary breakthroughs in data security.