ZK, Privacy, AND FHEs These three cryptographic ideas often get lumped together as if they are one thing. It is interesting that many people still treat ZK and privacy as the same, partly because in many systems, they work together and reinforce each other. FHE starts from a similar goal, but uses a different path. In practice, they each answer a different question: → ZK asks: “How do I convince everyone this was done correctly without showing them everything?” → Privacy asks: “Who is allowed to learn what, and at what point in the system?” → FHE asks: “How do I let an untrusted computer work on my data without ever seeing it?” The market grouped them under “privacy tech”, and I want to separate them in order of understanding and development. Read along 👇 -------- 📌First: ZK As A Proof Engine Before A Privacy Feature From first principles, zero-knowledge proofs (ZKPs) are about proof. You should understand this if you read my piece on the history of ZK. They exist because verifying a computation directly is expensive. Instead of everyone replaying the same work, a proof system replaces “re-run the whole computation” with “check this small object instead.” It gives you completeness, soundness, and zero knowledge. That is the core behaviour of ZK. You will see this in: → Rollups proving a batch of transactions in one proof. → Coprocessors like Brevis or Axiom proving a query on old state. → Bridge or crosschain systems that use Succinct style provers with Cysic hardware to ensure some state on chain A is valid before acting on chain B. They all use the same pattern. One side runs the full logic, the rest of the system only sees inputs, outputs, and a proof that connects them. --------- 📌 Second: Privacy As A system Like I said earlier, if ZK cares about “how do I prove this,” privacy cares about “who gets to learn what, and when.” This is a layout problem, and ZK is one tool in it. However, privacy itself is a system design that cuts across four surfaces: → Identity Surface: Which keys or accounts are visible, and how easily an observer can link them back to the same person. → Data Surface: Which balances, positions, orders, and messages are exposed in plain text. → Flow Surface: Who talks to whom, in what order, and how easy it is to trace that flow across time. → Compute surface: Which machines get to see raw data while executing logic. Most “privacy tech” is just moving one of these surfaces around. Some designs, like Monero, hide identities at the protocol level. You will never see a clean link between the person who spent and the person who received. The inputs are mixed with decoys, receivers get one-time style addresses, and outsiders only see that the totals still balance. Some designs, however, separate public and private activity. Think Zcash. You keep a normal, transparent pool for compatibility, and a shielded pool for when you want sender, receiver, and amount to disappear from public view, while proofs make sure nothing breaks. Then, we have some designs that focus on the flow of actions. These are Mixers and Tumblers. They try to make it harder for anyone watching the network to match “this person is about to do this trade,” or to trace a straight line between liquidity flows. We have private intents, internal matching, delayed broadcasts, and encrypted mempools working on that surface. Finally, some designs move the question to the execution layer. They care about who gets to see your raw data when the system is running logic. That is where multi-party computation, hardware enclaves, or encrypted execution show up. They change who needs to be trusted while the work is being done. When you look at it this way, privacy is simply a pattern you design. ZK, FHE, MPC, TEEs, and network-level protections are just ways of deciding what different observers are allowed to learn from your system. So, the privacy model is the arrangement, while the tools are how you enforce it. -------- 📌 Third: FHE As Encrypted Execution In most real designs today, ZK proofs and privacy systems still leave the last layer of execution exposed. A user or sequencer may hide inputs from the chain, and the network or prover may hide flows from observers, but the executor still sees your data before returning output. That weakens end-to-end privacy. People tried to close this gap with secure multi-party computation and trusted hardware environments, each with their own trust and performance tradeoffs. This is why Fully Homomorphic Encryption answers the question: “Can the machine that runs my code stay blind from start to finish?” With FHE, you encrypt your data once, send only ciphertext, and allow an untrusted machine to run the logic directly on that encrypted form. The machine will never learn your real inputs, and when you decrypt the final output, you will get the same result you would have gotten if the computation had been run normally. At first glance, it looks similar to ZK, but the axis is different. That is, ZK proves a result without showing the underlying data, while FHE produces the result without ever seeing the underlying data. If you want everyone else to trust that the encrypted execution followed the right rules, you can still add a ZK proof around it later. But the core of FHE is at execution, not verification. Below are examples of use cases: → A market that runs on FHE can update positions or balances without exposing them to validators. → A matching engine or intent layer can work on encrypted orders while only revealing final trades. → A credit or scoring system can process encrypted financial or identity data without anyone seeing the raw information. So if ZK is the tool that gives the world proof verification, FHE is the tool that executes data while everything stays hidden. The constraint FHE has is cost. The gates are very heavy, and every multiplication and bootstrapping step has a price, which slows adoption and scale. However, just as with ZK and privacy, there are improvements, and we will definitely see growth in the coming years. This is why Zama and Fhenix exist today. -------- 📌 The Triangle: ZK Privacy FHE When you put the three questions side by side, you realize that in designs, you rarely pick only one. For example: → A private payment system might keep balances encrypted with FHE, use ZK proofs to show that no extra money was created, and rely on stealth-style addresses and routing to ensure that outsiders do not see who paid whom. → A rollup might use ZK to compress a batch of transactions, use privacy at the RPC or intent layer to reduce MEV and tracing, and keep FHE for only a small set of sensitive operations, such as credit scoring or identity checks. Thirdly; → A crosschain protocol might use ZK as a light client to verify state on another chain, apply privacy patterns to avoid leaking which account is moving funds, and still keep some information or parameters encrypted under FHE so that no relayer ever sees them. From a marketing perspective, they all look like privacy techs because of the language used. “Trustless,” “Privacy preserving,” “Encrypted,” and “Zero knowledge” all signal that ZK appears in privacy systems, privacy systems use ZK and FHE, and FHE protocols promise to limit unwanted transparency. However, as explained above, they have distinct roles, share some design patterns, and amplify each other. One thing they cannot do is replace each other. That is why you should see them as a triangle. -------- 📌 A Memorandum Of Understanding Once you separate their roles, you gain clarity that: → A ZK rollup like zkSync or Starknet is not automatically a private chain. Same with ZK infra like Brevis, Axiom, Cysic, and Succinct. They can prove execution cheaply, but still need a privacy system on top if you do not want the world to see everything. → A privacy coin without a strong proof design and good handling of metadata can leak information or suffer from heavy verification costs. → An FHE chain without ZK or large economies of scale might keep everything encrypted, but still has to trust someone or something to ensure that computation is correct at all times. The purpose of this is to help us avoid lazy labeling. When you are researching a “privacy” token, you should know the right questions to ask. For example: → What proof engine does it use? → How is privacy arranged at each surface? → Which parts, if any, are actually using encrypted execution? -------- 📌 The Driver Of The Next Cycle ZK, Privacy, and FHEs will become the center for Web3 growth in the coming months and years. This is because, as more users and institutions move on-chain, they will want to use private systems with zero-knowledge and encryption. The goal is to prove the truth, control visibility, and compute on secrets, while the crypto economy grows. Thanks for reading!