Exploring Fully Homomorphic Encryption: A Privacy Protection Tool in the AI Era

Recently, although the cryptocurrency market is not as spectacular as in previous years, there are still some emerging technologies gradually maturing. Among them, fully homomorphic encryption (FHE) is a technology worth paying attention to.

To understand the seemingly complex concept of FHE, we first need to comprehend the meanings of "encryption" and "homomorphic", as well as why we need "fully".

The Basic Concepts of Encryption

Encryption is a common method of protecting information security. For example, if Alice wants to transmit a confidential message "1314 520" to Bob through a third party, she can use a simple numerical multiplication by 2 encryption method to change the message to "2628 1040". When Bob receives it, he only needs to divide each number by 2 to decrypt and obtain the original message. This symmetric encryption method allows for the secure transmission of information without trusting the third party.

Features of Homomorphic Encryption

Homomorphic Encryption goes a step further, allowing calculations on encrypted data without the need to decrypt first. Imagine a scenario: 7-year-old Alice needs to calculate her electricity bill for 12 months, at 400 yuan per month, but she cannot do complex calculations and does not want to disclose the specific amount. She can encrypt the numbers (for example, multiply them all by 2) and then ask someone to calculate the result of 800×24. After getting 19200, Alice simply divides by 4 to find out that the actual electricity bill is 4800 yuan. This is a simple example of multiplicative homomorphic encryption.

The Necessity of Fully Homomorphic Encryption

However, simple Homomorphic Encryption may be cracked. Fully Homomorphic Encryption makes it almost impossible to break by introducing more complex noise and allowing for arbitrary numbers of addition and multiplication operations. It can handle more complex polynomial computations, capable of expressing the vast majority of mathematical problems in the world, not just limited to simple calculations.

Fully homomorphic encryption did not make significant progress until 2009, becoming an important achievement in the field of encryption.

The Application of FHE in the AI Field

FHE technology has broad application prospects in the field of AI. Currently, training AI models requires a large amount of data, but many data are highly sensitive. FHE can solve this contradiction:

1. Use FHE encryption for sensitive data
2. Train AI using encryption data
3. AI output encryption results
4. Users securely decrypt results locally

This approach not only protects data privacy but also does not affect the computational capabilities of AI, achieving the goal of "both and."

Practical Application Cases of FHE

In practical applications, FHE can be used to solve sensitive issues such as facial recognition. It allows machines to determine whether it is a real person while not accessing any sensitive facial information.

However, FHE computation requires enormous computational power. To this end, some projects are building dedicated computing networks and supporting facilities. For example, a certain project proposed a network architecture that combines the characteristics of PoW and PoS, and launched dedicated hardware and NFT assets similar to "work certificates."

The Significance of Fully Homomorphic Encryption for AI Development

If FHE technology can be widely applied in the AI field, it will bring a great blessing to AI. Currently, many countries focus on data security and privacy protection in their regulation of AI. The application of FHE can fundamentally address these issues, allowing AI to exert its powerful capabilities while protecting privacy.

From national security to personal privacy, the importance of FHE technology is self-evident. In the upcoming AI era, FHE is likely to become the last line of defense for humanity in protecting data privacy. With the continuous development of technology, we look forward to seeing FHE play its unique role in more fields, contributing to the security and privacy protection of the digital world.