Fully Homomorphic Encryption FHE: Building a Privacy Fortress in the Era of AI

Recently, the market has been sluggish, giving us more time to focus on the development of some emerging technologies. Although the cryptocurrency market in 2024 is not as spectacular as in previous years, there are still some new technologies gradually maturing, including the topic we will discuss today: Homomorphic Encryption (Fully Homomorphic Encryption, abbreviated as FHE).

To understand the complex concept of FHE, we need to gradually analyze the meanings of the keywords "encryption", "homomorphic", and "fully".

Basic Concepts of Encryption

The most basic encryption methods are familiar to everyone. For example, if Alice wants to send a secret message "1314 520" to Bob, but has to go through a third party C to deliver it. To ensure the security of the information, Alice can use a simple encryption method: multiply each number by 2. This way, the message becomes "2628 1040". When Bob receives it, he just needs to divide each number by 2 to restore the original message. This symmetric encryption method allows for the transmission of information without revealing its content.

Homomorphic Encryption Advanced

Now, let us envision a more complex scenario:

Alice is only 7 years old this year, and she can only do the simplest multiplication and division by 2. The electricity bill at Alice's home is 400 yuan per month, and they have owed it for a total of 12 months, but she cannot perform such complex calculations. At the same time, she does not want others to know the specific amount of the electricity bill and the number of months owed.

In this case, Alice can utilize the principles of Homomorphic Encryption to delegate calculations to C while protecting her privacy. She multiplies 400 and 12 by 2 respectively, and then lets C calculate the result of 800 multiplied by 24. C quickly arrives at the answer of 19200. Alice then divides this result by 4, obtaining the actual electricity bill of 4800 yuan.

This is a simple example of Homomorphic Encryption for multiplication. 800 multiplied by 24 is actually a mapping of 400 multiplied by 12, and the form remains consistent before and after encryption, hence it is called "homomorphic". This method allows Alice to delegate computations to an untrusted third party without revealing sensitive information.

The Necessity of Fully Homomorphic Encryption

However, problems in the real world are often more complex. If C is clever enough, they might be able to crack Alice's original data through exhaustive search. At this point, it is necessary to introduce "fully homomorphic encryption" technology.

Fully homomorphic encryption is not just simple multiplication, but rather introduces more noise and complex mathematical operations, making decryption extremely difficult. It allows for arbitrary numbers of addition and multiplication operations on encrypted data, rather than being limited to specific operations.

This technology did not achieve breakthrough progress until 2009, when new ideas proposed by scholars such as Gentry opened the door to the possibilities of fully homomorphic encryption.

The Application Prospects of FHE in the AI Field

FHE technology has tremendous application potential in the field of AI. It is well-known that powerful AI systems require massive amounts of data for training, but much of this data has high privacy value. FHE technology allows AI systems to compute and learn while protecting data privacy.

Specifically, users can perform FHE encryption on sensitive data and then hand it over to the AI system for processing. The AI system will output a string of seemingly meaningless encrypted results. However, since these results follow specific mathematical rules, the data owner can securely decrypt them locally to obtain valuable analytical results.

This method resolves the contradiction of "utilizing the powerful computing capabilities of AI while protecting data privacy." Unlike current AI systems that must directly access raw data, FHE technology provides new possibilities for privacy protection in the AI era.

Practical Applications of FHE Technology

FHE technology has potential application scenarios in various fields. For example, in the field of facial recognition, FHE can help achieve the requirement of "determining whether it is a real person without accessing any sensitive facial information."

However, FHE computation requires substantial computing power support. To address this issue, some projects are building dedicated computing power networks and supporting facilities. These networks typically adopt a hybrid architecture similar to PoW (Proof of Work) and PoS (Proof of Stake) to provide the necessary computing resources.

The Significance of FHE Technology

If AI can widely apply FHE technology, it will have a profound impact on the entire industry. Currently, many countries focus their regulation of AI on data security and privacy protection. The maturity of FHE technology may become the key to solving these issues.

From national security to personal privacy, the application range of FHE technology is extremely broad. In the upcoming AI era, FHE is likely to become the last line of defense for protecting human privacy, and its importance is self-evident.