Fully Homomorphic Encryption: The Privacy Protection Tool of the AI Era

The recent cryptocurrency market has been sluggish, giving us more time to focus on the development of some emerging technologies. Although the market fluctuations in 2024 are not as intense as in previous years, there are still some new technologies that are gradually maturing, including the fully homomorphic encryption (FHE) that we are going to discuss today.

To understand the complex concept of FHE, we need to first clarify what "encryption", "homomorphic", and why "fully" is necessary.

Encryption Basics

The simplest encryption method is very familiar to us. For example, if Alice wants to send a secret number "1314 520" to Bob, but does not want a third party to know the content, she can use a simple encryption method: multiply each number by 2. In this way, the transmitted information becomes "2628 1040". When Bob receives it, he only needs to divide each number by 2 to get the original information.

This symmetric encryption method allows two parties to securely exchange information without trusting the messenger.

Homomorphic Encryption Concept

Now, let's consider a more complicated scenario. Suppose Alice is only 7 years old and only knows the most basic multiplication and division. She needs to calculate the electricity bill for 12 months at home, which is 400 yuan per month, but she cannot handle such complex multiplication.

Alice didn't want others to know the specific electricity bill and the number of months, so she used a clever method. She multiplied both 400 and 12 by 2, and then asked a person C who could perform complex calculations to help calculate the result of 800 multiplied by 24. C calculated the result as 19200, and after telling Alice, she divided this result by 4 (which is dividing by 2 twice), thus obtaining the correct total electricity bill of 4800 yuan.

This is a simple example of multiplicative homomorphic encryption. 800 multiplied by 24 is actually a mapping of 400 multiplied by 12, and the form remains unchanged before and after encryption, hence it is called "homomorphic". This method allows Alice to delegate computations to an untrusted third party while protecting sensitive information.

Why is "fully" homomorphic encryption necessary

However, problems in the real world are often more complex. If C is smart enough, they might crack the number that Alice originally intended to calculate through exhaustive search. This requires more advanced "fully homomorphic encryption" technology to solve.

Fully homomorphic encryption allows for performing arbitrary numbers of addition and multiplication operations on encrypted data, not limited to specific operations or a finite number of times. This greatly increases the difficulty of decryption, making it almost impossible for third parties to spy on the original data.

Fully homomorphic encryption technology did not achieve breakthrough progress until 2009, becoming an important milestone in the field of encryption.

Applications of FHE: An Example with AI

One important application area of FHE technology is artificial intelligence. It is well known that powerful AI systems require a large amount of data for training, but this data often involves privacy issues. FHE provides a possible solution to this contradiction:

1. Encrypt sensitive data using the FHE method.
2. Train the AI model with the encrypted data
3. AI outputs the encrypted results

This method allows AI to perform calculations and learn without ever accessing the original data. Data owners can securely decrypt the results locally, leveraging the powerful computing power of AI while protecting data privacy.

Currently, multiple projects are exploring the application of FHE technology in the field of AI. One of the projects proposed a very interesting application scenario: facial recognition. It can both allow machines to determine whether it is a real person and ensure that no sensitive facial information is leaked.

However, the practical applications of FHE still face some challenges, mainly because it requires substantial computational resources. To address this, some projects are building dedicated computing networks and supporting facilities to support FHE computation.

The Significance of FHE

If AI can widely apply FHE technology, it will greatly alleviate the current pressures of data security and privacy protection faced by AI development. From national security to personal privacy, FHE may become the last line of defense in protecting data in the era of AI.

With the rapid development of AI technology, we can foresee that in the near future, FHE technology may play an important role in more fields, providing stronger privacy protection while we enjoy the conveniences of AI.