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

Recently, although the cryptocurrency market has been performing mediocrely, there are still some emerging technologies gradually maturing, among which fully homomorphic encryption (FHE) is a noteworthy technology. In May of this year, Ethereum founder Vitalik Buterin also published an article about FHE, sparking widespread discussion in the industry.

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

The Basic Concepts of Encryption

Encryption is a commonly used method to protect information security. For example, Alice wants to send the message "1314 520" to Bob through a third party C, without letting C know the content. She can use a simple symmetric encryption method, multiplying each number by 2, resulting in "2628 1040". After receiving it, Bob can divide by 2 to decrypt and obtain the original message. This method allows for secure communication without trusting the intermediary.

Homomorphic Encryption Principles

Homomorphic encryption takes a step further by allowing specific computations to be performed on encrypted data without the need for decryption. For example, Alice needs to calculate the total electricity bill of 400 yuan for 12 months, but she cannot perform complex calculations. She can encrypt 400 and 12 by multiplying each by 2, allowing a trusted computing party C to compute the result of 800×24. After C obtains 19200, Alice can divide it by 4 to get the correct answer of 4800 yuan. Throughout this process, C does not know the actual amount of the electricity bill or the number of months.

The Necessity of Fully Homomorphic Encryption

However, simple homomorphic encryption is easily cracked. Fully homomorphic encryption significantly increases the difficulty of cracking by introducing more complex noise and allowing arbitrary numbers of addition and multiplication operations. It can handle more complex mathematical problems, not just simple calculations. In 2009, new ideas proposed by Gentry and other scholars opened the way for the realization of fully homomorphic encryption.

The Application of FHE in the AI Field

FHE technology has broad application prospects in the AI field. AI models require a large amount of data for training, but much of this data is highly private. FHE can allow AI to compute and learn while protecting data privacy:

1. Use FHE encryption for sensitive data
2. Input the encryption data into AI for computation.
3. AI outputs encryption results
4. Users locally securely decrypt the results

This method allows AI to complete tasks without accessing the original data, effectively resolving the conflict between data privacy and AI development.

Development of FHE Projects

Currently, there are several projects related to FHE, such as Zama, Privasea, and Mind Network. Taking Privasea as an example, this project proposes application scenarios like facial recognition, which can identify real people without disclosing sensitive information. In order to address the high computational power requirements of FHE, Privasea has designed special network architectures and hardware devices.

The Significance and Prospects of FHE

With the popularization of AI technology, data privacy issues are becoming increasingly prominent. From national security to personal privacy, FHE technology may become the last line of defense for data protection. It not only facilitates the compliant development of AI but also plays an important role in various sensitive scenarios.

In the next decade, AI may deeply integrate into our lives. If FHE technology can truly mature, it will provide a powerful tool for humans to protect privacy in the AI era.