Crypto Assets Market Overview and Homomorphic Encryption Technology Development

As of October 13, a certain data platform's statistics on major Crypto Assets show:

The discussion heat for Bitcoin last week was 12.52K times, a decrease of 0.98% compared to the previous week. The closing price on Sunday was 63916 USD, an increase of 1.62% year-on-year.

The discussion heat for Ethereum last week was 3.63K times, an increase of 3.45% compared to the previous week. The closing price on Sunday was $2530, a year-on-year decrease of 4%.

The discussion popularity of TON last week was 782 times, a decrease of 12.63% compared to the previous week. The closing price on Sunday was $5.26, a slight drop of 0.25% year-on-year.

Homomorphic Encryption ( FHE ) is a promising technology in the field of cryptography. It allows computations to be performed directly on encrypted data without decryption, providing strong support for privacy protection and data processing. FHE can be widely applied in various 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.

The Potential and Application Scenarios of FHE

The biggest advantage of Homomorphic Encryption is privacy protection. For example, when a company needs to utilize another company's computing power to analyze data but does not wish for the other party to access the specific content, FHE can come into play. The data owner can transmit the encrypted data to the computing party for processing, and the computation results remain encrypted; the data owner can decrypt it afterward to obtain the analysis results. This mechanism protects data privacy while accomplishing 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 increasingly become a focal point. FHE can provide multi-party computation protection in these scenarios, allowing all 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 review functions.

Comparison of FHE with Other Encryption Methods

In the Web3 field, FHE, zero-knowledge proofs ( ZK ), multiparty computation ( MPC ), and trusted execution environments ( TEE ) are the main privacy protection methods. Unlike ZK, FHE can perform various operations on encrypted data without needing to decrypt it first. MPC allows parties to compute while the data is encrypted, without sharing private information. 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

Although the theoretical foundation of FHE is strong, it faces practical challenges in commercial applications:

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

2. Limited operational capability: Although FHE can perform addition and multiplication on encrypted data, its support for complex nonlinear operations is limited, which is 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 restrictions 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 the multi-key FHE framework allows for 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 is widely applied in multiple fields, but concerns over data privacy often make users reluctant to share sensitive information. FHE provides a privacy protection solution for the AI domain. In cloud computing scenarios, data is usually encrypted during transmission and storage, but it is often in plaintext during processing. With FHE, user data can 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 data processing methods and ensure that data is protected during transmission. FHE's end-to-end encryption provides assurance for compliance and data security.

Current Application 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, 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. This company focuses on Boolean operations and low-word-length integer operations based on TFHE technology, and has developed an FHE development stack aimed at blockchain and AI applications.

Other projects include:

• Develop a new type of smart contract language and HyperghraphFHE library for projects on blockchain networks.
• A project that implements privacy protection in AI computing networks using FHE.
• A project that combines FHE with artificial intelligence to provide a decentralized and privacy-preserving AI environment.
• As an Ethereum Layer 2 solution, it supports FHE Rollups and FHE Coprocessors, is EVM compatible, and supports Solidity for writing smart contracts.

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 revolutionary breakthroughs to data security.