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

Recently, although the cryptocurrency market has not been very volatile, some emerging technologies are gradually maturing. Among them, fully homomorphic encryption (FHE) is a technology worth paying attention to. In May of this year, Ethereum founder Vitalik Buterin also published an article discussing the relevant content of FHE.

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

Basic Concepts of Encryption

Encryption is a common method for protecting information security. Suppose Alice wants to send a secret message "1314 520" to Bob via a third party C. She can use a simple symmetric encryption method, multiplying each number by 2, resulting in "2628 1040". After receiving it, Bob only needs to divide each number by 2 to decrypt and obtain the original information. In this way, even if C participates in the information transmission process, they cannot know the actual content.

Homomorphic Encryption Advanced

Homomorphic Encryption goes a step further by allowing computations to be performed directly on encrypted data without needing to decrypt it first. For example, suppose 7-year-old Alice only knows how to perform simple multiplication by 2 and division by 2. She needs to calculate her family's electricity bill for 12 months (400 yuan per month), but she doesn't want others to know the exact amount.

Alice can encrypt 400 and 12 by multiplying them by 2, resulting in 800 and 24, and then ask C to help calculate 800×24. After C calculates the result as 19200 and tells Alice, Alice then divides the result by 4 to get the correct total electricity bill of 4800 yuan. Throughout this process, C does not know what is actually being calculated, which reflects the characteristics of Homomorphic Encryption.

The Necessity of Fully Homomorphic Encryption

However, simple Homomorphic Encryption still has limitations. For example, if C is smart enough, it may crack the original data through exhaustive methods. In addition, simple Homomorphic Encryption can only support a limited number of specific operations.

Fully Homomorphic Encryption (FHE) breaks through these limitations. It allows for arbitrary additions and multiplications to be performed on encrypted data, and can express most mathematical problems in the world. FHE introduces more complex noise, making it nearly impossible for third parties to decrypt the original data, truly achieving a win-win situation for data usage and privacy protection.

The Application Prospects of FHE

FHE technology has broad application prospects in the field of AI. Currently, AI training requires a large amount of data, but much of this data is highly sensitive. FHE allows AI to compute and learn from encrypted data while protecting data privacy.

Specifically, users can provide sensitive data to the AI system after encrypting it with FHE. The AI system processes the encrypted data and outputs results that are also encrypted. Users can then decrypt these results in a secure local environment. This method ensures that the AI has sufficient training data while also guaranteeing that user privacy is not compromised.

Challenges and Developments of FHE Technology

Despite the promising prospects of fully homomorphic encryption (FHE), practical applications still face challenges. The main issue is that FHE computations require substantial computational power support. To address this problem, some projects are exploring the establishment of dedicated computational networks and supporting infrastructure.

With the continuous advancement of technology, Fully Homomorphic Encryption (FHE) is expected to become a key technology for protecting data privacy in the AI era. From personal privacy protection to national security, FHE may play an important role. In the future AI-driven world, FHE technology may become the last line of defense for safeguarding the data security of individuals and organizations.