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Zero-knowledge proofs let an application verify a specific claim about private data without revealing that data itself. In blockchain development, they can support private-attribute checks, validity-proven rollups, and proofs of execution—but they do not automatically hide a transaction or make an entire application anonymous.
What does a zero-knowledge proof prove?
A zero-knowledge proof is a way for a prover to convince a verifier that a defined statement is true without disclosing information beyond the statement’s truth. The prover uses a witness: private input or data that supports the claim. The verifier checks the proof against the statement and any public inputs without needing to see the witness. Ethereum.org traces the concept to a 1985 paper and describes this standard definition on its zero-knowledge proofs page.
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For example, an application could ask a user to prove they meet an age threshold without sending a passport number to the verifier. The proof only establishes the claim encoded in the application; it does not prove unrelated facts about the user. Which details remain private depends on what the statement and public inputs disclose.
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“Zero knowledge” describes what a proof reveals about its witness, not every data flow around the proof. Public transaction inputs and outputs, timing, contract calls, and linked metadata may expose information. Public blockchain addresses are pseudonymous, not inherently anonymous, and other data can associate an address with a real identity. Ethereum.org explains these limits alongside its overview of zero-knowledge proofs.
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- Specify exactly what the verifier is being asked to accept.
- Identify each public input and consider what it reveals when combined with onchain events or outside data.
- Review what the application publishes before and after proof verification, not just what the proof omits.
How do blockchain applications use ZK proofs?
Scaling with a ZK-rollup
A ZK-rollup executes transactions off Ethereum mainnet and uses an onchain contract to manage rollup state. An operator batches transactions, produces a validity proof that the state transition followed the rollup’s rules, and submits the proof and relevant public inputs. The contract verifies them before accepting the updated state root. Ethereum.org describes this flow in its ZK-rollup documentation.
Inputs to verification can include the pre-state root, post-state root, batch root, and transaction inputs. The proof supports the claim that valid transactions moved the rollup from the old state to the new one; it does not mean those transactions are secret. Rollups publish transaction data or state differences so independent parties can reconstruct the state. That data availability supports permissionless verification, but is not a privacy mechanism.
Private attributes and transactions
A privacy-oriented identity application can let someone prove a qualifying attribute—such as citizenship or being above a required age—without exposing a tax ID or passport details. Privacy-focused chains, including Zcash and Monero, use zero-knowledge technology to validate transactions without requiring access to transaction details. These are application patterns, not a guarantee that every implementation hides all metadata or transaction information. See Ethereum.org’s discussion of zero-knowledge applications and limits.
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Proving execution
A zkEVM for Ethereum Layer 1 block verification is a different design from a zkEVM rollup used as a Layer 2 scaling system. In the L1 verification model, a prover executes a block’s transactions and produces a SNARK or STARK proof of correct execution, which validators can verify instead of independently re-executing every transaction. Ethereum.org explains the distinction on its zkEVM for L1 block verification page.
The Ethereum Foundation’s July 10, 2025 post describes a staged path in which validators could verify proofs from multiple zkVM implementations. It discusses an expected approach and adoption path, not a claim that all Ethereum validators already use it. Read the Foundation’s realtime-proving post.
How should you plan a ZK implementation?
Start with the claim and the data boundary, then select a proof system and decide where proving and verification will happen. Those decisions shape what users reveal, who pays for computation, and what assumptions the application relies on.
- Define the statement. Write down the precise condition the verifier must accept, and identify the private witness that supports it.
- Map public inputs and disclosures. List the values sent to the verifier or published onchain. Consider whether their combination with timestamps, events, or external records could reveal sensitive information.
- Match the proof system to the workload. Assess witness size, setup requirements, proof size, proving resources, and verification cost. Do not select a family on a generic claim that it is always faster or cheaper.
- Choose where proof generation runs. Proving can require substantial computation, so decide whether it runs on a user’s device, a server, or another environment and who bears that cost.
- Choose where verification runs. Onchain verification adds gas costs. Client-side verification shifts responsibility toward the client, including code integrity and repeated-verification considerations.
- Review setup and security assumptions. Determine whether the selected construction requires a trusted setup. If it does, examine the ceremony design, participant assumptions, and independent review relevant to the implementation.
Ethereum.org’s educational secret-state tutorial demonstrates a server/client workflow with an onchain verifier for a blockchain game. It uses proofs to verify game results and deploys a verification contract; it is an example to learn from, not a complete production architecture.
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The tutorial’s version table identifies ZoKrates 1.1.9, TypeScript 5.4.2, Node 20.18.2, and Viem 2.9.20 as the versions used or verified for that tutorial. These are tutorial version labels, not a statement of the latest releases. It also names Circom and Cairo as alternatives that may fit different requirements.
The tutorial simplifies repeated compilation and key generation. In production, compiled programs and keys may be reused safely rather than regenerating them for every operation. Reuse and key management should be designed around the chosen system’s security requirements.
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SNARK or STARK: which trade-offs matter?
SNARK and STARK refer to families of proof systems, not single interchangeable implementations. Their practical properties depend on the specific construction, workload, prover, verifier, and deployment. Ethereum.org’s comparison describes these general trade-offs:
| Decision axis | SNARK considerations | STARK considerations |
|---|---|---|
| Setup assumptions | Some SNARK constructions use a common reference string produced through a trusted setup. Security depends on ceremony entropy and the destruction of secret contributions. | In the model Ethereum.org describes, STARKs use publicly verifiable randomness rather than a trusted setup. |
| Proof size | Ethereum.org characterizes SNARK proofs as succinct and generally smaller than STARK proofs. | STARK proofs are larger, which can increase verification overhead. |
| Workload scaling | Proving and verification behavior depends on the concrete system and workload; there is no universal performance result. | Ethereum.org describes more favorable proving and verification scaling as witness size grows, with larger proof overhead as a trade-off. |
| Security assumptions | Elliptic-curve-based SNARKs can rely on assumptions that future quantum capabilities could affect. This is not a settled claim about every construction. | Ethereum.org describes STARK security as based on collision-resistant hash functions and calls it resistant to the quantum threat cited on its page; that characterization is scoped to that security model. |
| Onchain verification | Cost depends on the system and deployment. Ethereum.org gives an illustrative Ethereum figure discussed below. | Larger proof sizes can increase verification overhead; actual application costs require measurement for the selected implementation. |
A useful choice follows from the application’s constraints: setup assumptions, acceptable proof size, witness scale, proving resources, and verifier cost. Compare concrete implementations against those requirements instead of treating either family as the universal winner.
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Proving and verification resources
Generating a proof involves complex calculations and may require specialized, expensive hardware. The compute burden can affect user experience or shift costs to an application operator. Verification also consumes computation; when it happens onchain, that work can add transaction costs.
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Ethereum.org’s documentation gives approximately 500,000 gas as an illustrative figure for verifying a single ZK-SNARK proof on Ethereum and notes that STARKs may require higher fees. This is a 2026 documentation example, not a universal benchmark or a current quote for a particular contract. Actual gas depends on the implementation and network conditions. The figure and qualification appear in Ethereum.org’s proof-system discussion and ZK-rollup documentation.
Trusted-setup risk
For constructions that use a trusted setup, the common reference string is security-sensitive. Ethereum.org explains that if a dishonest prover controls the setup entropy, false proofs may be possible. A multi-party ceremony reduces this risk if at least one participant honestly deletes their secret contribution. The ceremony model and its assumptions therefore belong in the security review; the existence of a ceremony by itself is not proof that a particular deployment is safe.
Privacy and availability boundaries
A validity proof establishes that a computation or state transition satisfies specified rules. It does not conceal inputs or outputs that the application publishes separately. In rollups, publishing enough data for independent state reconstruction enables verification, while also meaning that publication should not be mistaken for confidentiality.
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Quick Recap
What should a team verify before shipping?
- The statement, witness, and public inputs match the intended application behavior.
- Published inputs, outputs, events, and timing do not undermine the privacy goal.
- The selected system’s proof-size, proving, and verification requirements fit the target workload.
- Setup assumptions and ceremony practices are understood where applicable.
- The location and cost of proof generation are acceptable for users or the service operator.
- Verification placement is intentional, including its gas, client integrity, and repeated-check implications.
- Implementation-specific costs and security properties have been reviewed for the actual deployment rather than inferred from a general comparison.
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