Crypto Assets Market Dynamics and Homomorphic Encryption Technology Development

According to the latest data statistics, as of October 13th, the discussion heat and price performance of major Crypto Assets are as follows:

The discussion frequency of Bitcoin last week was 12.52K times, a slight 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 volume on Ethereum last week reached 3.63K, an increase of 3.45% compared to the previous week. However, its price on Sunday was $2530, a decrease of 4% compared to the same period last week.

The discussion count for TON coin last week was 782 times, a decrease of 12.63% compared to the previous week. Its price on Sunday was $5.26, a slight drop of 0.25% compared to the same period last week.

Homomorphic Encryption ( FHE ), as a cutting-edge technology in the field of cryptography, is showing great application potential. Its core advantage lies in the ability to perform computations directly on encrypted data without the need for decryption, providing strong support for privacy protection and data processing. FHE can be widely applied in multiple fields such as finance, healthcare, cloud computing, machine learning, voting systems, the Internet of Things, and blockchain privacy protection. Nevertheless, the commercialization of FHE still faces many challenges.

Advantages and Application Scenarios of FHE

The greatest advantage of Homomorphic Encryption lies in privacy protection. For example, when a company needs to leverage external computing power to analyze data but does not want the data content to be accessed externally, FHE can play a role. The company can encrypt the data before transmission, and the external agency can perform calculations in an encrypted state, with the results remaining encrypted. The original company can decrypt it later to 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 become increasingly important. FHE can provide multi-party computation protection in these areas, 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 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. Unlike ZK, FHE can perform various operations on encrypted data without needing to decrypt the data first. MPC allows parties to compute under the condition that data is encrypted, without each party sharing private information with one another. TEE provides computation in a secure environment, but has 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 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, there are practical challenges in its commercial applications:

1. High computational overhead: FHE requires significant computational resources, and its computational overhead increases significantly compared to unencrypted computation. For high-degree polynomial operations, the processing time grows polynomially, making it difficult to meet real-time computing demands. Reducing costs depends on dedicated hardware acceleration, but this also increases deployment complexity.

2. Limited operational capability: FHE can perform addition and multiplication on encrypted data, but support for complex nonlinear operations is limited, which poses a bottleneck for artificial intelligence applications involving deep neural networks. Current FHE schemes are still mainly suitable for linear and simple polynomial computations, 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 rises sharply when dealing with multi-user datasets. Although there are multi-key FHE frameworks that allow operations on encrypted datasets with different keys, 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 used in multiple 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, FHE allows user data to 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 review. Currently, several 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 blockchain privacy protection projects.
• A certain project is based on TFHE technology, focusing on Boolean operations and low-word-length integer operations, and has built an FHE development stack for blockchain and AI applications.
• Another project has developed a new smart contract language and HyperghraphFHE library, suitable for blockchain networks.
• A certain project utilizes FHE to achieve privacy protection in AI computation networks, supporting various AI models.
• There are projects that combine FHE with artificial intelligence, providing a decentralized and privacy-preserving AI environment.
• A certain Layer 2 solution supports FHE Rollups and FHE Coprocessors, is compatible with EVM, and supports 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. Moreover, with the development of blockchain technology, FHE will play an increasingly important role in privacy protection and secure computing. In the future, FHE could become the core technology supporting privacy-preserving computations, bringing revolutionary breakthroughs in data security.