Crypto Assets Market Data Analysis and Homomorphic Encryption Technology Discussion

As of October 13, a certain data platform conducted a statistical analysis of the discussion heat 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 price reached 63916 dollars last Sunday, an increase of 1.62% compared to two weeks ago.

The discussion around Ethereum has increased in intensity, with 3.63K discussions last week, an increase of 3.45% compared to the previous week. However, its price has declined, closing at $2530 last Sunday, which is a 4% decrease from two weeks ago.

The performance of TON coin has been relatively weak, with a noticeable decline in discussion heat. The number of discussions last week was only 782, a decrease of 12.63% compared to the previous week. In terms of price, there was also a slight drop, with the daily price quoted at $5.26 last week, down 0.25% from two weeks ago.

In the field of encryption technology, Homomorphic Encryption (FHE) is gradually becoming a highly regarded technology. It allows for computations to be performed directly on encrypted data without the need for decryption, which holds great potential for privacy protection and handling sensitive data. FHE can be applied in various fields including finance, healthcare, cloud computing, machine learning, voting systems, the Internet of Things, and blockchain privacy protection. Nevertheless, the commercialization of FHE still faces numerous challenges.

The main advantage of FHE lies in privacy protection. For example, a company can hand over encrypted data to another company for analysis, which can perform calculations without knowing the original data content and ultimately return the encrypted result. This mechanism is particularly important for data-sensitive industries such as finance and healthcare, while also meeting the growing data security demands in the fields of cloud computing and artificial intelligence.

In the Web3 ecosystem, FHE is on par with zero-knowledge proofs, multi-party computation, and trusted execution environments as a primary privacy protection method. In comparison, FHE performs better in supporting complex computational tasks. However, FHE also faces practical issues such as high computational overhead and poor scalability, which limit its performance in real-time applications.

The main challenges faced by FHE in the commercialization process include:

1. Large-scale computational overhead: FHE requires a significant amount of computational resources, especially when dealing with high-degree polynomial operations, where the computation time grows polynomially.

2. Limited operational capability: Although FHE supports addition and multiplication of encrypted data, its support for complex nonlinear operations is limited, which poses a barrier to artificial intelligence applications.

3. Complexity of multi-user support: When involving multi-user datasets, the system complexity rises sharply, increasing the difficulty of key management and system architecture.

Despite these challenges, the combination of FHE and artificial intelligence still holds great promise. In the current data-driven era, FHE provides privacy-preserving solutions for AI, allowing sensitive data to be processed in an encrypted state while meeting compliance requirements such as GDPR.

In the blockchain field, FHE is mainly used to protect data privacy, including on-chain privacy, AI training data privacy, on-chain voting privacy, and privacy transaction review, among other aspects. Currently, multiple projects are utilizing FHE technology to advance the implementation of privacy protection, such as Zama, Octra, Privasea, MindNetwork, and Fhenix.

Overall, FHE, as an advanced technology that can perform computations on encrypted data, has significant advantages in protecting data privacy. Although it still faces some technical challenges at present, with the development of hardware acceleration and algorithm optimization, these issues are expected to be gradually resolved. In the future, FHE has the potential to become the core technology supporting privacy-preserving computation, bringing revolutionary breakthroughs in data security.