Fully Homomorphic Encryption (FHE)
Fully Homomorphic Encryption (FHE) is a cryptographic technology that allows computations to be performed directly on encrypted data, or
ciphertext, without needing to decrypt it first
12. This capability is often described as one of the "holy grails" of cryptography
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The "fully" in FHE signifies that it supports an
unlimited number of both addition and multiplication operations on the encrypted data, which means any arbitrary computation can be performed while preserving privacy
14. Only the owner of the key can decrypt the final result of the computation
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How FHE Works
FHE extends public key cryptography, operating on the principle that for every function performed on unencrypted text (
Plaintext), there is an equivalent function for encrypted text (
Ciphertext)
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The process generally follows these steps
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- Encryption: An unencrypted input (A) is encrypted using an FHE scheme 25.
- Processing: The encrypted data is sent to a processing node (e.g., a cloud server or a research institution) 5.
- Computation: The node performs computations directly on the encrypted data, resulting in an encrypted output (B) 5. Crucially, the node never sees the raw, unencrypted data 25.
- Decryption: The encrypted result (B) is sent back to the key owner, who can then decrypt it to obtain the final usable output 5.
This mechanism ensures that sensitive information can be processed without ever being revealed to the third party performing the computation
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Categories of Homomorphic Encryption
FHE is the most advanced of four general categories of homomorphic encryption, which differ based on the types and limits of operations they support
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The idea for FHE was initially proposed in 1978 by Rivest, Adleman, and Dertouzous, but the first practical FHE scheme was proposed in 2009 by Craig Gentry
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Applications and Use Cases
FHE is considered a transformative technology for secure data processing in untrusted environments, such as cloud computing platforms
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Key applications include:
- Secure Cloud Computing: Organizations can leverage third-party cloud services for data processing without compromising the security of their data, as the data remains encrypted throughout the process 4. For example, Nasdaq incorporated FHE to analyze trading patterns across different brokers without revealing proprietary algorithms or sensitive data 4.
- Privacy-Preserving Data Analysis: FHE allows institutions to conduct studies on sensitive data, such as medical records, without ever seeing the patient's actual health information 2.
- Blockchain and Decentralized Computing: FHE is an emerging privacy solution in the blockchain space 6. It can be used to implement privacy-preserving light clients 3. In the crypto ecosystem, FHE is being integrated into:
- FHE-Powered L1s (Layer 1 Blockchains) like Fhenix and Inco 6.
- Applications such as zkHoldem (gaming), Penumbra, and Elusiv (DeFi) 6.
- Tools and Middleware like Sunscreen and ZAMA, which provide foundational infrastructure for FHE implementation 6.
In blockchain implementations, FHE often uses
threshold decryption schemes and
multi-party computation (MPC) to distribute the decryption keys among multiple nodes, preventing any single entity from accessing all encrypted data on the network
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