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

Although the cryptocurrency market has been relatively calm recently, some emerging technologies are gradually maturing. Among them, fully homomorphic encryption (FHE) is a noteworthy field. In May of this year, Ethereum founder Vitalik Buterin specifically published an article on FHE, which sparked widespread discussion in the industry.

To understand the complex concept of FHE, we need to start from the basics and understand what "encryption", "homomorphic", and why we need to be "fully".

Basic Concepts of Encryption

The simplest encryption method is something we are all familiar with. For example, if Alice wants to send Bob a secret number "1314 520", but doesn't want a third party to know the content, she can use a simple encryption rule: multiply each number by 2. This way, the transmitted message becomes "2628 1040". When Bob receives it, he just needs to divide each number by 2 to decrypt the original information. This is a basic symmetric encryption process.

Characteristics of Homomorphic Encryption

Homomorphic Encryption takes it a step further. Suppose Alice only knows the most basic operations of multiplying by 2 and dividing by 2, but she needs to calculate a complex electricity bill: 400 yuan per month, owed for 12 months. Alice cannot handle such complicated multiplication, but she does not want others to know the specific amount. So she can do this: multiply 400 by 2 to get 800, multiply 12 by 2 to get 24, and then have a trusted calculator help her calculate 800 multiplied by 24. After the calculator arrives at the result of 19200, Alice divides by 2 twice to get the correct answer of 4800 yuan.

This is a simple example of Homomorphic Encryption for multiplication. It allows computations to be performed on encrypted data without the need for decryption. This method enables delegating computations to untrusted third parties while keeping sensitive data secure.

The Necessity of Fully Homomorphic Encryption

However, simple homomorphic encryption has its limitations. For example, if the calculator is smart enough, it may be able to crack the original data through exhaustive methods. This requires a more complex encryption method, namely fully homomorphic encryption.

Fully homomorphic encryption allows arbitrary addition and multiplication operations on encrypted data, not limited to specific operations. This greatly increases the difficulty of decryption, enabling even complex polynomial calculations to be completed while protecting privacy.

Fully homomorphic encryption did not achieve breakthrough progress until 2009. The new ideas proposed by researchers like Gentry opened up new possibilities for this technology.

The Application of FHE in the AI Field

The FHE technology has broad application prospects in the field of AI. It is well known that powerful AI systems require vast amounts of data for training, but much of the data is highly sensitive. FHE can effectively resolve this contradiction:

1. Encrypt sensitive data using FHE method
2. Train AI models with encrypted data
3. AI outputs the encryption result

Unsupervised AI models can directly handle this encrypted data, as the input is essentially vectors for them. Meanwhile, data owners can securely decrypt the results locally. This achieves the goal of leveraging the powerful computing power of AI while protecting privacy.

Examples of FHE Projects

Currently, multiple projects are exploring FHE technology, such as Zama, Mind Network, and Fhenix. Taking a project invested by a certain trading platform as an example, it proposed an interesting application scenario: facial recognition. Through FHE technology, it is possible to determine whether it is a real person without accessing the original facial data.

However, FHE computation requires substantial computational power support. Therefore, the project proposed a hybrid PoW and PoS network architecture to address the computational power issue. Recently, they also launched dedicated mining hardware and a special NFT "work certificate", attempting to provide computational power incentives while circumventing regulatory risks.

The Importance of FHE

If AI can widely apply FHE technology, it will greatly alleviate the current pressures regarding data security and privacy protection. From national security to personal privacy, FHE could become an important means of protection.

In the upcoming AI era, the maturity of FHE technology may become the last line of defense for protecting human privacy. Whether in commercial applications or scientific research, FHE is expected to play an important role in the future.