Fully Homomorphic Encryption: Overview and Application Scenarios

fully homomorphic encryption(FHE) is a special encryption scheme that allows for direct function computation on ciphertext without decryption, thereby protecting privacy. Unlike traditional static encryption and encryption in transit, FHE can perform complex processing on ciphertext, which is particularly useful in privacy-preserving scenarios involving multi-party collaboration.

A typical application of FHE is in online voting systems. Voters can submit their encrypted votes to an intermediary entity, which can tally the votes without decrypting them, ultimately only announcing the final results. This avoids the problem of intermediaries needing to decrypt all ballots to count them, thereby better protecting voting privacy.

In an FHE system, the encryption function and computation process are public, but the input data and output results are encrypted. Only those who possess the decryption key can obtain the plaintext information. FHE is a compact encryption scheme where the size of the output ciphertext and decryption complexity depend only on the original input and do not rely on the complexity of the computation process.

FHE usually includes the following types of keys:

1. Decryption Key: The system's master key, used to decrypt FHE ciphertext, usually kept by the user locally.

2. Encryption Key: Used to convert plaintext into ciphertext, can be made public in public key mode.

3. Calculate Key: Used for homomorphic operations on ciphertext, and can also be made public.

The main application modes of FHE include:

1. Outsourcing Model: Outsourcing the computational tasks of sensitive data to cloud service providers while protecting data privacy.

2. Two-party computation model: Allows both parties to perform joint computations without revealing their respective private data.

3. Aggregation Mode: Securely aggregate data from multiple participants, suitable for scenarios such as federated learning.

4. Client-Server Model: The server provides private AI model computation services for multiple independent clients.

The main advantage of FHE compared to traditional encryption schemes is that it allows for complex computations on ciphertexts, bringing new possibilities for privacy protection. However, the computational overhead of FHE is still quite high, requiring further technological breakthroughs and dedicated hardware support to be practically applied in more widespread scenarios.