Introduction to Fully Homomorphic Encryption Technology and Application Scenarios

Fully homomorphic encryption ( FHE ) is a special encryption scheme that allows function computations directly on ciphertext without decryption, thereby protecting data privacy. Unlike traditional static encryption and transmission encryption, FHE can perform complex processing on ciphertext, making it suitable for privacy protection scenarios involving multi-party collaboration.

A typical application of FHE is online voting systems. Voters can encrypt their voting results and submit them to an intermediary, which can directly perform statistics on the encrypted data. After obtaining the final results, the intermediary can decrypt and announce them, without ever coming into contact with plaintext data. Compared to solutions that rely on trusted third parties or hardware isolation, FHE provides security guarantees purely at the software level.

FHE systems typically include the following types of keys:

1. Decryption Key: Master Key, used to decrypt FHE ciphertext, usually kept only by the user locally.

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

3. Calculating the key: used for performing homomorphic operations on ciphertext, can be public but will not disclose the original data.

Common application patterns of FHE include:

1. Outsourcing Model: Users outsource encrypted data and computing tasks to cloud service providers, suitable for scenarios such as private information retrieval.

2. Both parties calculation mode: Both parties hold their private data and perform joint calculations through FHE without disclosing the original data.

3. Aggregation Mode: Summarizes the encrypted data provided by multiple parties for federated learning, voting statistics, etc.

4. Client-Server Model: The server provides a private AI model, and the client submits encrypted data for inference.

The advantage of Fully Homomorphic Encryption (FHE) lies in its security being based on cryptographic algorithms rather than hardware, making it resistant to side-channel attacks. However, it has a relatively high computational overhead and is currently mainly used in scenarios focused on linear computations. In the future, with the development of dedicated hardware, FHE is expected to find applications in more fields.