Analysis of the Development of the Crypto Assets Market and Homomorphic Encryption Technology

As of October 13, a data platform has compiled statistics on the discussion trends and price changes of major Crypto Assets.

The discussion frequency of 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% compared to the same period last week.

The discussion heat for Ethereum reached 3.63K times last week, an increase of 3.45% compared to the previous week. However, its closing price on Sunday was 2530 USD, a decrease of 4% compared to the same period last week.

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

Homomorphic Encryption (Fully Homomorphic Encryption, FHE) is a cutting-edge technology in the field of cryptography, with its core advantage being the ability to perform computations directly on encrypted data without the need for decryption. This feature provides strong support for privacy protection and data processing. The application scope of FHE is broad, covering various fields such as finance, healthcare, cloud computing, machine learning, electronic voting, the Internet of Things, and blockchain privacy protection. Despite the enormous potential of FHE, it still faces many challenges in the commercialization process.

Application Prospects of FHE

The greatest advantage of FHE lies in its excellent privacy protection capability. Imagine a scenario where Company A needs to utilize Company B's computing resources to analyze data, but does not want Company B to access the original data content. In this case, FHE can play a key role: Company A can encrypt the data and send it to Company B for processing, and the computation results remain encrypted. After receiving the results, Company A decrypts them to obtain the required analysis information. This mechanism protects data privacy while meeting computational needs.

For industries with extremely high sensitivity to data, such as finance and healthcare, the value of FHE is particularly prominent. With the rapid development of cloud computing and artificial intelligence technologies, data security has increasingly become the focus of all parties. FHE can provide multi-party computation protection in these fields, allowing all participants to collaborate without exposing sensitive information. Especially in blockchain technology, FHE significantly enhances the transparency and security of data processing by enabling on-chain privacy protection and privacy transaction review functions.

Comparison of FHE and Other Encryption Technologies

In the Web3 ecosystem, FHE, Zero-Knowledge Proofs (ZK), Multi-Party Computation (MPC), and Trusted Execution Environments (TEE) are the main privacy protection solutions. The uniqueness of FHE lies in its ability to perform various operations on encrypted data without the need to decrypt it first. MPC allows multiple parties to compute while keeping the data in an encrypted state, without needing to share private information with each other. TEE provides a secure computing environment but is relatively limited in terms of 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 issues of high computational overhead and poor scalability in practical applications, which limits its performance in real-time application scenarios.

Limitations and Challenges of FHE

Despite the solid theoretical foundation of FHE, there have been some practical difficulties in the commercialization process:

1. The consumption of computing resources is enormous: FHE requires a large amount of computing resources, and its computational overhead significantly increases compared to unencrypted computation. Especially for high-degree polynomial operations, the processing time grows polynomially, making it difficult to meet real-time computing needs. To reduce costs, FHE often relies on specialized hardware acceleration, which adds to the complexity of deployment.

2. Limited operational capability: Although FHE can perform addition and multiplication on encrypted data, its support for complex nonlinear operations is limited. This poses a bottleneck for artificial intelligence applications involving deep neural networks. Current FHE schemes are primarily suitable for linear and simple polynomial computations, with significant restrictions on the application of nonlinear models.

3. Multi-user support complexity: FHE performs well in single-user scenarios, but the system complexity rises sharply when it involves multi-user datasets. Although the multi-key FHE framework proposed in 2013 allows operations on encrypted datasets with different keys, the complexity of key management and system architecture increases significantly.

The Integration of FHE and Artificial Intelligence

In the current data-driven era, Artificial Intelligence (AI) is widely used in multiple fields, but due to data privacy concerns, users are often reluctant to share sensitive information, such as medical and financial data. FHE provides a privacy-protecting solution for the AI field. In cloud computing scenarios, data is typically encrypted during transmission and storage, but is often in plaintext during processing. With FHE, user data can be processed while remaining encrypted, ensuring data privacy.

This advantage is particularly important under regulations such as GDPR, which require users to have the right to know how their data is processed and ensure that data is protected during transmission. The end-to-end encryption of FHE provides guarantees for compliance and data security.

The Current Application Status of FHE in Blockchain

The application of FHE in the blockchain field mainly focuses on data privacy protection, including on-chain privacy, AI training data privacy, on-chain voting privacy, and on-chain privacy transaction review, among others. Currently, several projects are leveraging FHE technology to promote the realization of privacy protection.

The FHE solution developed by a certain company is widely used in multiple blockchain projects. This company focuses on Boolean operations and low-width integer operations based on TFHE technology, and has built an FHE development stack for blockchain and AI applications.

Other projects are also actively exploring the application of FHE:

• A company has developed a new smart contract language and a special FHE library for blockchain networks.
• Another company utilizes FHE to achieve privacy protection in AI computing networks, supporting various AI models.
• Some companies are combining FHE with artificial intelligence to provide a decentralized and privacy-preserving AI environment.
• A certain project serves as a Layer 2 solution 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 enables computation 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 computation. In the future, FHE is expected to become a core technology supporting privacy-preserving computation, bringing revolutionary breakthroughs in data security.