Fully Homomorphic Encryption FHE: Introduction and Application Scenarios

Encryption is generally divided into two types: static encryption and encryption in transit. Static encryption involves encrypting data before storage, allowing only authorized individuals to view the decrypted content. Encryption in transit ensures that data transmitted over the internet can only be interpreted by designated individuals. Both types of encryption rely on encryption algorithms and provide guarantees of data integrity, referred to as "authenticated encryption."

In scenarios of multi-party collaboration that require complex processing of ciphertext, fully homomorphic encryption ( FHE ) is a privacy protection technology. Taking online voting as an example, FHE allows for the counting of encrypted ballots without decrypting the ciphertext, thereby protecting privacy.

FHE is a special encryption scheme that allows function computation on ciphertext without decryption. The computation process is public and can be executed in the cloud without leaking privacy. Both input and output are encrypted ciphertexts, requiring a key for decryption.

The FHE outsourcing model is considered an alternative to secure execution environments such as TEE. The security of FHE is based on cryptographic algorithms and does not rely on hardware, thus it is not affected by side-channel attacks or attacks on cloud servers.

FHE usually uses several sets of keys:

1. Decryption Key: Main Key, used to decrypt FHE ciphertext.
2. Encryption Key: used to convert plaintext into ciphertext.
3. Calculate the key: used for performing homomorphic operations on ciphertext.

There are several common application modes for FHE:

1. Outsourcing Model: Outsourcing computing tasks to cloud servers, protecting data privacy.

2. Two-party computation model: Both parties contribute private data for joint computation.

3. Aggregation Mode: Aggregates data from multiple parties for scenarios such as federated learning.

4. Client-Server Model: The server provides FHE computation services for multiple clients.

FHE can ensure the validity of computation results by introducing redundancy, digital signatures, and other methods. The distribution of the decryption key can enhance security through methods such as secret sharing.

Overall, FHE is a powerful privacy-preserving technology that enables complex computational tasks while protecting data privacy, with the potential to play a significant role in areas such as cloud computing and federated learning.