Fully Homomorphic Encryption FHE: Decrypting Data Privacy Protection in the AI Era

Recently, the cryptocurrency market has slowed down, giving us more time to focus on the development of some emerging technologies. Among them, fully homomorphic encryption (Homomorphic Encryption, abbreviated as FHE) is a maturing technology that deserves our in-depth discussion. In May of this year, Ethereum founder Vitalik Buterin also published an article specifically about FHE, which has attracted widespread attention.

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

The Basic Concepts of Encryption

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

Homomorphic Encryption Advanced

Now, let's assume that Alice is only 7 years old and can only perform the most basic multiplication and division by 2. She needs to calculate the electricity bill for her home, which is 400 yuan per month for 12 months, but this exceeds her calculation ability. At the same time, she does not want others to know the specific amount of the electricity bill.

At this point, homomorphic encryption comes into play. Alice can transform 400 multiplied by 2 into 800, and 12 multiplied by 2 into 24, and then ask C to calculate the result of 800 multiplied by 24. After calculating 19200, C informs Alice, who then divides the result by 4 to obtain the correct total electricity bill of 4800 yuan.

This process demonstrates the core of Homomorphic Encryption: performing computations in an encrypted state, with the result decrypted to be the same as directly calculating the original data.

The Necessity of Fully Homomorphic Encryption

However, the above method still has vulnerabilities. If C is clever enough, they might be able to crack the original data through reverse engineering or brute force methods. This requires a more complex encryption method, namely fully homomorphic encryption.

Fully homomorphic encryption allows arbitrary numbers of addition and multiplication operations to be performed on encrypted data, rather than being limited to a specific number or type of operations. This greatly increases the difficulty of decryption while expanding the range of problems that can be addressed.

In 2009, Gentry and other scholars proposed a new approach that first achieved fully homomorphic encryption, which is regarded as a significant breakthrough in the field of cryptography.

The Application of FHE in the AI Era

Fully homomorphic encryption technology has broad application prospects in the field of AI. Currently, training AI models requires a large amount of data, but much of this data is highly sensitive. FHE technology allows AI to process encrypted data while protecting data privacy.

Specifically, users can:

1. Encrypt sensitive data using fully homomorphic encryption.
2. Provide the encrypted data to AI for computation.
3. AI returns the encryption result
4. The user decrypts the results in a local secure environment.

This approach ensures the privacy and security of data while fully utilizing the powerful computing capabilities of AI.

FHE Projects and Application Directions

Currently, several projects are dedicated to the development and application of FHE technology, such as Zama, Mind Network, Fhenix, and Sunscreen. Each of these projects has its own characteristics, exploring the potential applications of FHE in different scenarios.

Taking a certain FHE project as an example, it proposes a very interesting application scenario: facial recognition. Through FHE technology, it is possible to determine whether a person is real without accessing the original facial data. This method not only protects user privacy but also meets the needs of identity verification.

Challenges and Solutions of FHE

Although FHE technology has a broad prospect, it still faces huge computational resource demands in practical applications. To address this issue, some projects are building dedicated computing power networks and supporting facilities.

For example, a certain project proposed a hybrid network architecture that combines PoW (Proof of Work) and PoS (Proof of Stake), and launched dedicated mining hardware and NFT assets. This innovative design attempts to provide the necessary computing power while avoiding certain legal risks.

The Significance of FHE for AI and Privacy Protection

If FHE technology can be widely applied in the AI field, it will greatly alleviate the current pressures of data security and privacy protection faced by AI development. From national security to personal privacy, FHE could become a crucial means of protection.

In an increasingly digital world, data privacy issues are everywhere, from international conflicts to various aspects of daily life. With the rapid development of AI technology, the maturity of FHE technology may become the last line of defense in protecting human privacy.