How large is the potential of FHE (Fully Homomorphic Encryption)?
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How large is the potential of FHE (Fully Homomorphic Encryption)?
Although FHE (Fully Homomorphic Encryption) is unlikely to mature and be widely adopted in the short term, it serves as an extension and complement to ZKP technology.
Navigating Web3 tides with focused insightsAuthor: Haotian
Vitalik's recent article on FHE (Fully Homomorphic Encryption) has once again sparked imagination and exploration around new cryptographic technologies. In my view, FHE indeed represents a significant leap beyond ZKP in terms of potential, enabling broader real-world applications at the intersection of AI and crypto. How should we understand this?
1) Definition: FHE allows computations to be performed directly on encrypted data in specific formats without exposing the underlying data or privacy. In contrast, ZKP only solves the problem of consistent data transmission under encryption—verifying parties can confirm the authenticity of submitted data, making it a point-to-point encrypted transmission solution. FHE, however, does not restrict who performs the computation, and thus can be seen as a many-to-many encrypted computing framework.
2) How it works: Traditional computing operates on plaintext data; if data is encrypted, it must first be decrypted before processing, inevitably risking privacy exposure. Homomorphic encryption creates a special cryptographic scheme that enables "homomorphic" transformations on ciphertexts such that the result of operations on encrypted data matches what would be obtained from operating on plaintext. For example, in homomorphic systems, plaintext addition corresponds to ciphertext multiplication (a defined rule), so performing addition on plaintext data simply requires multiplying the ciphertexts—an equivalent operation.
In short, homomorphic encryption uses specially designed transformations to allow computations on encrypted data to yield results identical to those on plaintext, as long as the homomorphic correspondence between operations is preserved.
3) Applications: In traditional internet domains, FHE can be widely applied in cloud storage, biometric identification, healthcare, finance, targeted advertising, genetic sequencing, and more. Take biometrics: personal data such as fingerprints, irises, or facial features are highly sensitive. With FHE, servers can compare and verify such data while it remains encrypted. Similarly, in healthcare, where data silos have persisted for years, FHE can enable joint analysis and modeling across institutions without requiring raw data sharing.
In the crypto space, FHE opens possibilities in gaming, DAO governance voting, MEV protection, private transactions, and regulatory compliance—any scenario demanding strong privacy. In gaming, for instance, platforms could perform game-state calculations without seeing players' hidden cards, enhancing fairness.
In DAO voting, large stakeholders ("whales") could participate in governance without revealing their addresses or vote amounts, while the protocol still computes accurate results. Users could submit encrypted transactions to the mempool, preventing exposure of destination addresses and transfer amounts. In regulatory contexts, governments could monitor fund pools and isolate illicit assets (e.g., from blacklisted addresses) without accessing the private details of legitimate transactions.
4) Limitations: It’s important to note that conventional plaintext computing environments support complex operations beyond basic arithmetic—such as conditional loops and logical gates. Current homomorphic encryption schemes, including partially and fully homomorphic variants, efficiently support only addition and multiplication. More complex operations require layering these basic functions, significantly increasing computational demands.
Thus, while FHE theoretically supports arbitrary computations, performance bottlenecks and algorithmic constraints severely limit the types and complexity of operations that can be executed efficiently today. Complex computations incur substantial computational overhead. As such, the practical deployment of FHE hinges on advancements in algorithm optimization and cost-effective computing power—particularly improvements driven by hardware acceleration and enhanced processing capabilities.
In summary,
While FHE may remain challenging to deploy at scale in the near term, I believe it serves as a powerful extension and complement to ZKP technology. It holds great potential to advance privacy-preserving AI—enabling secure AI model computation, collaborative data modeling, joint training—and to expand privacy-compliant transactions and use cases in crypto.
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Haotian | CryptoInsight
