Crypto Assets Market Trends and Homomorphic Encryption Technical Analysis

As of October 13, the discussion heat and price trends of several major Crypto Assets show different situations. The number of discussions about Bitcoin last week was 12.52K, slightly down 0.98% from the previous week, but the price reached $63,916 on Sunday, up 1.62% compared to the previous week. Ethereum's discussion heat has risen, increasing 3.45% to 3.63K discussions, but its price fell by 4% to $2,530. The attention on TON coin has noticeably decreased, with discussions down 12.63% to 782 times, and the price also slightly dropped by 0.25% to $5.26.

Homomorphic Encryption (FHE), as a cutting-edge technology in the field of cryptography, is gradually attracting widespread attention. Its core advantage lies in the ability to perform calculations directly on encrypted data without the need for a decryption process, providing strong support for privacy protection and data processing. This technology has potential application value in various fields such as finance, healthcare, cloud computing, and machine learning, especially in scenarios requiring high data confidentiality.

The application scenarios of FHE are very broad. For example, in data collaboration between companies, one company can use the computing resources of another company to analyze data while ensuring the confidentiality of the data content. In industries such as finance and healthcare, where data sensitivity is extremely high, the privacy protection mechanism of FHE is particularly important. With the rapid development of cloud computing and artificial intelligence, data security has increasingly become a focal issue, and FHE can achieve secure multi-party computation in these areas, allowing all parties to collaborate without disclosing private information.

In the Web3 ecosystem, FHE, along with technologies such as zero-knowledge proofs, multi-party computation, and trusted execution environments, constitutes the main privacy protection schemes. Compared to other methods, FHE excels in supporting complex computational tasks, allowing various operations on encrypted data without the need for decryption. However, FHE also faces challenges such as high computational overhead and poor scalability, which limit its performance in real-time applications.

The main obstacles encountered by FHE in practical applications include: the resource consumption required for large-scale computations is enormous, and the overhead increases significantly compared to unencrypted calculations; there is limited support for complex nonlinear operations, which becomes a bottleneck in AI applications such as deep neural networks; in multi-user scenarios, system complexity rises sharply, increasing the difficulty of key management and architecture design.

Nevertheless, the combination of FHE and artificial intelligence still shows great potential. In the current data-driven era, AI technology is widely applied across various fields, but data privacy issues have always been a focal point of concern for users. FHE provides a privacy protection solution for AI, ensuring that sensitive data remains encrypted during processing in the cloud, which is particularly important for businesses that need to comply with data protection regulations like GDPR.

In the field of blockchain, FHE is mainly applied to protect data privacy, including on-chain privacy, AI training data privacy, on-chain voting privacy, and privacy transaction review. Currently, several projects are utilizing FHE technology to advance the realization of privacy protection. For example, a company has developed a solution based on TFHE technology, focusing on Boolean operations and low-bit-length integer operations, and has built an FHE development stack for blockchain and AI applications. Other projects have developed new smart contract languages and FHE libraries to meet the needs of blockchain networks.

Some projects are dedicated to applying Homomorphic Encryption (FHE) to privacy protection in AI computing networks, supporting various AI models. Some projects go further, combining FHE with artificial intelligence to provide a decentralized and privacy-preserving AI environment. In the Ethereum ecosystem, Layer 2 solutions supporting FHE Rollups and FHE Coprocessors have also emerged, compatible with EVM and supporting smart contracts written in Solidity.

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