The real-world applications of zero-knowledge proofs in Web3: from privacy protection to performance breakthroughs

Zero-Knowledge Proofs (Zero-Knowledge Proof) sound profound and mysterious, but they are quietly changing the game rules of blockchain. From privacy transactions to Layer2 scaling, the application scope of this technology is much broader than most people imagine.

What exactly does Zero-Knowledge Proof solve?

Simply put, a zero-knowledge proof allows one party to prove that a statement is true without revealing any specific information.

Imagine you want to prove to a friend that you have more than one million in savings, but you don’t want to disclose your bank account. This is where zero-knowledge proof comes in—you only need to prove “I have over one million” without exposing account details, specific numbers, or transaction records.

This concept was first proposed by Shafi Goldwasser and Silvio Micali at MIT in 1985. They discovered that the prover and verifier can establish trust by exchanging minimal information. In simple terms: both parties can reach consensus without revealing privacy.

Why does blockchain urgently need zero-knowledge proofs?

Privacy issues are becoming increasingly severe. Traditional centralized platforms harvest user data and store personal identity information (PII) in centralized databases. Once attacked, data leaks lead to rampant scams. Transactions on public chains are transparent, which is a nightmare for users requiring financial privacy.

Performance bottlenecks restrict development. In traditional blockchains, each transaction must be verified repeatedly—signature validation, legality checks, smart contract execution. With zero-knowledge proofs, the same computation only needs to be proven once, greatly reducing computational load. This is one of the key technologies for Layer2 scaling.

Trust costs are too high. In decentralized systems, verifiers need to obtain大量数据 to confirm transaction validity. Zero-knowledge proofs make verification easier and more efficient.

From privacy coins to DeFi, zero-knowledge proofs are already being implemented

Anonymous transaction scenarios are the most intuitive. Privacy coins like Zcash and Monero use zero-knowledge proof technology to shield the sender, receiver, asset type, and amount. Users can transact completely anonymously, and on-chain nodes can verify validity without seeing transaction details.

On public chains like Ethereum, Tornado Cash provides a decentralized, non-custodial mixing service. It uses zero-knowledge proofs to obfuscate transaction details, allowing users to achieve private transfers on public chains. Although later shut down due to regulatory issues, this case demonstrates the feasibility of the technology.

Identity verification is also quietly transforming. Traditional methods require submitting sensitive information like name, email, date of birth. But with zero-knowledge proofs, users only need to prove “I am an adult” or “I am a member of a certain platform” without revealing specific identities. For example, proving you are over 18 without showing your ID, just generating a ZK proof.

Verifiable computation is unleashing computing power. When local computation costs are too high, users can delegate calculations to third parties (like oracle services). Zero-knowledge proofs enable providers to prove their computation results are correct, allowing users to trust the result without re-computing.

Anonymous voting becomes possible. Under the premise of completely concealing identities, users can still prove they have voting rights and cast votes.

How does zero-knowledge proof work: from color-blind games to Sudoku verification

The workflow of zero-knowledge proofs must satisfy three core elements: completeness, soundness, and zero-knowledge.

• Completeness: If the statement is true, honest verifier will be convinced
• Soundness: If the statement is false, no cheater can fool the verifier
• Zero-knowledge: The verifier learns nothing except that the statement is true

Based on the verification method, zero-knowledge proofs are divided into two main categories:

Interactive schemes require multiple rounds of dialogue. Take the classic “color-blind game”: Alice is color-blind, Bob is not. Bob has two identical balls, one blue and one red. Bob wants to prove to Alice that the colors are different.

The process: Alice hides the two balls behind her back, randomly swaps their positions, then asks Bob “Did you swap?” If Bob can see the colors, he will give the correct answer. Repeating this multiple times, if Bob answers correctly each time, Alice’s confidence increases (the probability of being convinced after n rounds is 1 - (1/2)^n).

But this method has obvious flaws: each verification starts from scratch, both parties must be present simultaneously, and when multiple verifiers are involved, the entire process must be repeated.

Non-interactive schemes generate a permanent proof in one go. Understanding this with a Sudoku example: Alice solves a difficult puzzle and wants to prove it to Bob. She puts the solution into an tamper-proof machine, which follows a public protocol:

1. Extract each row’s 9 cards, shuffle, and place into a bag (9 bags)
2. Extract each column’s 9 cards, shuffle, and place into a bag (9 bags)
3. Extract each 3×3 grid’s cards, shuffle, and place into a bag (9 bags)

Total of 27 bags. Bob checks whether each bag contains the digits 1-9. If all pass, he is convinced Alice truly solved the Sudoku, and he learns nothing about the specific solution.

This scheme’s advantages are clear: verification only needs one round, the proof is permanent, and anyone can verify using the same proof.

Two major technical schemes competing and evolving

Currently, the most common in Layer2 is zk-rollup architecture—bundling multiple transactions and publishing an “validity proof” to Layer1, proving these transactions are valid.

zk-SNARK is a “succinct non-interactive argument of knowledge.” It uses elliptic curve cryptography to generate compact proofs that are easy to verify. On Ethereum, verifying a single zk-SNARK proof costs about 500,000 gas, which is relatively low. Projects using zk-SNARK include Zcash, Loopring, zkSync 1.0/2.0, Zigzag, Mina, etc.

Advantages: low gas cost. Disadvantages: high hardware requirements and trust assumptions (participants’ input data must be trusted).

zk-STARK is a “transparent, scalable zero-knowledge proof.” Compared to SNARK, STARKs have advantages such as:

• Shorter proof times
• Easier scalability
• Hash-based, quantum-resistant
• No trusted setup

Disadvantages: higher verification costs. StarkWare (StarkEx, StarkNet) and Immutable X are using this scheme.

Zero-knowledge proofs still face practical challenges

High hardware costs. Generating zero-knowledge proofs involves large-scale vector multiplications, FFTs, etc., with about 70% of the time spent on multi-scalar multiplication (MSM). This requires specialized hardware acceleration, typically FPGA (three times cheaper than GPU, over ten times more energy-efficient).

Verification is also expensive. Verifying zk-STARKs can be even more costly than SNARKs, posing challenges for large-scale applications.

Trust assumptions trap. zk-SNARKs require initial parameters to be correctly generated, but users cannot evaluate the honesty of the participants. If false data is input, users are forced to trust. zk-STARKs do not have this issue, but researchers are working on non-trusted setup for SNARKs to improve security.

Quantum computing threats are approaching. zk-SNARKs rely on elliptic curve signatures (ECDSA), which are currently secure but could be cracked by quantum computers. zk-STARKs use hash functions resistant to quantum attacks.

Future imagination for zero-knowledge proofs

The true value of zero-knowledge proofs lies in: inheriting the security of underlying blockchains (like Ethereum) while greatly improving DApp performance and user privacy. Packing transactions on-chain reduces costs, while user data remains off-chain, ultimately making Web3 applications fast, secure, and private.

This technology is no longer purely theoretical; it is reshaping blockchain infrastructure. Whether for privacy transactions, identity verification, or Layer2 scaling, zero-knowledge proofs are playing an increasingly important role.