Let's talk about Zama again, the shining child of FHE. Today, we will discuss the war between the two trust models: FHE and ZK, a point that is rarely elaborated on. Two trust philosophies in the world of encryption. If cryptography is a revolution about "trust," then ZK (Zero-Knowledge) and FHE (Fully Homomorphic Encryption) are two factions in this revolution. ZK represents a "proof-based trust": I perform the calculation and then prove to you that I got it right, but I don't tell you how I did it. FHE, on the other hand, is "encryption-based trust": I don't even want you to see the calculation process; you only get the result. The former says: "Trust that I calculated correctly." The latter says: "You can't see what I'm calculating at all." It sounds like just half a step apart, but in reality, they represent two completely different worldviews. ZK is privacy in a transparent world; FHE is order in a black-box world. ZK: The Power of Proof ZK (Zero-Knowledge Proof) has been the most dazzling star in cryptography in Web3 over the past five years. Its magic lies in allowing one party to prove that a statement is true without revealing the data. For example: You can prove "I have 1 ETH in my wallet" without showing your wallet balance. This is incredibly useful in the blockchain world. The logic of ZK is typically "public proof, private computation" — calculating on your own computer, posting the proof result on the chain, and others can verify it instantly. Thus, we have the entire industry of zkRollup, zkVM, zkEVM, etc. ZK addresses the question of **"Can I trust the correctness of the computation?"** Its core is high verification efficiency. Verification takes just a few milliseconds, proof generation may take a few seconds, but once generated, the whole world can quickly confirm it. This is very suitable for scenarios like public chains, exchanges, bridges, etc. However, its drawbacks are also evident: Data must be "pre-verifiable" — everything must be encoded into the proof circuit. It does not support true "confidential computation" — the data itself ultimately needs to be publicly verified. High complexity in scaling — as the circuit becomes more complex, costs rise exponentially. In summary: ZK is the "notary office" of the crypto world; it ensures you calculated correctly, but everyone can see what you are proving. FHE: The Ambition of Black-Box Computation In contrast, FHE (Fully Homomorphic Encryption) represents another ambition. It does not prove that you calculated correctly; it directly allows you to compute on ciphertext. You encrypt the data into a bunch of unreadable ciphertext, but this ciphertext can still be computed, such as addition, multiplication, averaging. The final result, once decrypted, is exactly the same as the result of "plaintext computation." What does this mean? It means you can perform operations entirely in an encrypted state. Servers, contracts, AI models do not know what data they are processing. This is a complete elimination of trust. No intermediaries, no risk of leakage, and no need for proof. But the cost is also astonishing: Each computation is as slow as a snail, and bootstrapping consumes a lot. For the past decade, FHE has been stuck at a performance bottleneck. Until the emergence of Zama @zama_fhe. They have begun to make FHE practical through "programmable bootstrapping" and "fhEVM." ZK pursues the speed of verification; FHE pursues the purity of privacy. Performance and Boundaries: Who is Closer to Reality? In terms of performance, ZK is currently winning handsomely. Fast verification, mature ecosystem, and established infrastructure (zkSync, StarkNet, Scroll, etc. L2). FHE is still in the early stage of "20 TPS." But in terms of privacy completeness, FHE is a more thorough solution. ZK still needs to expose part of the computation logic to prove, while FHE can complete the entire computation in ciphertext. So, is their combination the ultimate weapon? Hybrid: zkFHE, the Potential Ultimate Privacy Form The combination of ZK + FHE (also known as zkFHE) is becoming a new research direction in academia and industry. The logic of this model is: Use FHE for private computation, allowing data to run in ciphertext; Use ZK to prove that the computation is indeed correct, preventing deception. It's like a safe with a camera — you don't know what's going on inside, but you can confirm it's doing the right thing. This can simultaneously solve two major problems: FHE is too slow → using ZK verification can simplify it; ZK is not private enough → using FHE can complete the privacy. Zama @zama_fhe is also exploring this direction: fhEVM retains symbolic execution, co-processor verification, and provable execution interfaces, laying down system-level interfaces for the future zkFHE. This is the ultimate fusion of trust and privacy: a "verifiable ciphertext world."