Fully Homomorphic Encryption Technology FHE: A Bridge Between Privacy Protection and AI Development

Recently, the market has had relatively small fluctuations, giving us more time to focus on some emerging technologies. Although the cryptocurrency market in 2024 is not as lively as in previous years, there are still some new technologies maturing, such as "fully homomorphic encryption" (Fully Homomorphic Encryption, abbreviated as FHE), which we will discuss today.

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

Basic Concepts of encryption

Encryption is a method of protecting information security. Suppose Alice wants to send a secret number "1314 520" to Bob, but it must be transmitted through a third party C. To keep it confidential, Alice can multiply each number by 2, turning it into "2628 1040". After receiving it, Bob only needs to divide each number by 2 to restore the original information. This is a simple symmetric encryption method.

The Principle of Homomorphic Encryption

Homomorphic Encryption goes a step further, allowing computations on encrypted data without the need for decryption. For example, Alice needs to calculate her electricity bill for 12 months, at 400 per month, but she does not want others to know the exact amount. She can encrypt 400 and 12 by multiplying each by 2, letting C compute 800×24. After C arrives at the result of 19200, Alice can divide it by 4 to get the correct answer of 4800. Throughout this process, C does not know what is actually being calculated.

The Necessity of Fully Homomorphic Encryption

However, simple homomorphic encryption may be broken. Fully homomorphic encryption introduces more complex mathematical operations, making it nearly impossible to break the encrypted data while allowing for an arbitrary number of addition and multiplication operations to be performed in the encrypted state. This makes fully homomorphic encryption an important breakthrough in cryptography.

The Application of FHE in the AI Field

FHE technology has broad application prospects in the AI field. AI requires a large amount of data for training, but this data often involves privacy. FHE allows AI to perform computations and learn from encrypted data while protecting data privacy. For example:

1. Users can provide sensitive data to the AI after encryption.
2. AI performs calculations and learns on encrypted data.
3. AI outputs encryption results.
4. The user decrypts the result locally to obtain the necessary information.

This method protects user privacy while not affecting the functionality of AI.

Practical Application Scenarios of Fully Homomorphic Encryption

FHE technology can be applied in multiple fields, such as facial recognition. It is capable of determining whether a person is real without accessing the original facial data. This resolves the contradiction between privacy protection and functionality.

However, FHE computation requires enormous computing power. To address this issue, some projects are building dedicated computing networks and supporting facilities. For example, a certain project has launched hardware similar to mining equipment and a special NFT to support the operation of its FHE network.

The Significance of FHE for AI Development

If FHE technology can be widely applied in the AI field, it will greatly promote the development of AI. Currently, many countries focus their regulation of AI mainly 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 protection, FHE technology has potential application space. In an era of rapid AI development, FHE may become the last line of defense for protecting human privacy.