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

Recently, although the cryptocurrency market has been relatively sluggish, some emerging technologies are gradually maturing. Among them, fully homomorphic encryption (简称FHE) is a cutting-edge technology worth paying attention to.

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

Basic Concepts of encryption

The simplest encryption method is well known to everyone. For example, if Alice wants to send a message like "1314 520" to Bob, but does not want intermediaries to know the content, she can encrypt it by multiplying each number by 2, resulting in "2628 1040". When Bob receives it, he only needs to divide each number by 2 to decrypt the original message. This is a basic symmetric encryption method.

Advanced Homomorphic Encryption

Homomorphic Encryption goes a step further. Suppose Alice can only perform simple operations of multiplying by 2 and dividing by 2, but she needs to calculate a complex multiplication, such as 400 multiplied by 12. To protect her privacy, she can multiply 400 and 12 by 2 to get 800 and 24 respectively, then have a trusted third party calculate 800 multiplied by 24. After obtaining the result of 19200, Alice divides it by 4 to get the correct answer of 4800. During this process, the third party does not know the original data but helps complete the calculation, which is the basic principle of Homomorphic Encryption.

The Necessity of Fully Homomorphic Encryption

However, simple homomorphic encryption carries the risk of being cracked. For example, someone with malicious intent may guess the original data through brute force. Fully homomorphic encryption introduces more complex mathematical operations and noise, significantly increasing the difficulty of cracking. It allows for an arbitrary number of addition and multiplication operations on encrypted data, rather than being limited to specific operations, which is what "fully" means.

In 2009, a new approach proposed by Gentry and other scholars paved the way for the development of fully homomorphic encryption technology. This technology is regarded as a significant breakthrough in the field of encryption.

The Application Prospects of FHE in the AI Field

FHE technology has broad application prospects in the field of AI. Currently, the development of AI is facing challenges related to data privacy and security. FHE allows AI models to process and learn from encrypted data while protecting user privacy.

Specifically, users can encrypt sensitive data using fully homomorphic encryption (FHE) and then provide it to AI for computation. The AI model will output a string of encrypted results that only the data owner can decrypt. This protects the privacy of the original data while leveraging the powerful computing capabilities of AI for analysis and prediction.

Practical Applications of FHE Technology

FHE technology can be applied in multiple fields, such as facial recognition. It can determine whether it is a real person without revealing the user's facial features. This demand for "both privacy protection and functionality realization" is precisely the advantage of FHE technology.

However, FHE computation requires massive computational power support. To address this issue, some projects are building dedicated computing networks and supporting facilities.

The Significance of FHE Technology

In the AI era, data privacy issues have become increasingly important. From national security to personal privacy, FHE technology may become the last line of defense in protecting data. If FHE technology can truly mature and be widely applied, it will clear many regulatory obstacles for the development of AI, while also providing users with a safer data usage environment.

With the advancement of technology, we may soon see the widespread application of FHE in various fields, bringing new possibilities for data security and privacy protection.