# What Is a Zero-Knowledge Proof?

While the inherent transparency of blockchains provides an advantage in many situations, there are also a number of smart contract use cases that require privacy due to various business or legal reasons, such as using proprietary data as inputs to trigger a smart contract’s execution. An increasingly common way privacy is achieved on public blockchain networks is through *zero-knowledge proofs (ZKPs)* — a method for one party to cryptographically prove to another that they possess knowledge about a piece of information without revealing the actual underlying information. In the context of blockchain networks, the only information revealed on-chain by a ZKP is that some piece of hidden information is valid and known by the prover with a high degree of certainty.

# Zero Knowledge vs. Zero Trust

“Zero knowledge” refers to the specific cryptographic method of zero-knowledge proofs, while “zero trust” is a general cyber security model used by organizations to protect their data, premises, and other resources.

The zero-trust framework assumes that every person and device, both internal and external to the network, could be a threat due to malicious behavior or simple incompetence. To mitigate threats, zero-trust systems require users and devices to be authenticated, authorized, and continuously validated before access to resources is granted.

Zero-knowledge proofs can be used as part of a zero-trust framework. For example, zero-knowledge authentication solutions can allow employees to access their organization’s network, without having to reveal personal details.

# How Do Zero-Knowledge Proofs Work

At a high level, a zero-knowledge proof works by having the verifier ask the prover to perform a series of actions that can only be performed accurately if the prover knows the underlying information. If the prover is only guessing as to the result of these actions, then they will eventually be proven wrong by the verifier’s test with a high degree of probability.

Zero-knowledge proofs were first described in a 1985 MIT paper from Shafi Goldwasser and Silvio Micali called “The Knowledge Complexity of Interactive Proof-Systems”. In this paper, the authors demonstrate that it is possible for a *prover *to convince a *verifier *that a specific statement about a data point is true without disclosing any additional information about the data. ZKPs can either be interactive — where a prover convinces a specific verifier but needs to repeat this process for each individual verifier — or non-interactive — where a prover generates a proof that can be verified by anyone using the same proof.

The three fundamental characteristics that define a ZKP include:

**Completeness:**If a statement is true, then an honest verifier can be convinced by an honest prover that they possess knowledge about the correct input.**Soundness:**If a statement is false, then no dishonest prover can unilaterally convince an honest verifier that they possess knowledge about the correct input.**Zero-knowledge:**If the state is true, then the verifier learns nothing more from the prover other than the statement is true.

# Zero-Knowledge Proof Example

A conceptual example to intuitively understand proving data in zero knowledge is to imagine a cave with a single entrance but two pathways (path A and B) that connect at a common door locked by a passphrase. Alice wants to prove to Bob she knows the passcode to the door but without revealing the code to Bob. To do this, Bob stands outside of the cave and Alice walks inside the cave taking one of the two paths (without Bob knowing which path was taken). Bob then asks Alice to take one of the two paths back to the entrance of the cave (chosen at random). If Alice originally chose to take path A to the door, but then Bob asks her to take path B back, the only way to complete the puzzle is for Alice to have knowledge of the passcode for the locked door. This process can be repeated multiple times to prove Alice has knowledge of the door’s passcode and did not happen to choose the right path to take initially with a high degree of probability.

After this process is completed, Bob has a high degree of confidence that Alice knows the door’s passcode without revealing the passcode to Bob. While only a conceptual example, ZKPs deploy this same strategy but use cryptography to prove knowledge about a data point without revealing the data point. With this cave example, there is an input, a path, and an output. In computing there are similar systems, and circuits, which take some input, pass the input signal through a path of electrical gates and generate an output. Zero-knowledge proofs leverage circuits like these to prove statements.

Imagine a computational circuit that outputs a value on a curve, for a given input. If a user is able to consistently provide the correct answer to a point on the curve, one can be assured the user possesses some knowledge about the curve since it becomes increasingly improbable to guess the correct answer with each successive challenge round. One can think of the circuit like the path that Alice walks in the cave, if she is able to traverse the circuit with her input, she proves she holds some knowledge, the “passcode” to the circuit, with a high degree of probability. Being able to prove knowledge about a data point without revealing any additional information besides knowledge of data provides a number of key benefits, especially within the context of blockchain networks.

# Types of Zero-Knowledge Proofs

There are various implementations of ZKPs, with each having its own trade-offs of proof size, prover time, verification time, and more. They include:

## zk-SNARKs

SNARKs, which stands for “succinct non-interactive argument of knowledge”, are small in size and easy to verify. They generate a cryptographic proof using elliptical curves, which is more gas-efficient than the hashing function method used by STARKS.

## zk-STARKs

STARK stands for “scalable transparent argument of knowledge”. STARK-based proofs require minimal interaction between the prover and the verifier, making them much faster than SNARKs.

## PLONK

Standing for “permutations over Lagrange bases for oecumenical non-interactive arguments of knowledge,” PLONKs use a universal trusted setup that can be used with any program and can include a large number of participants.

## Bulletproofs

Bulletproofs are short non-interactive zero-knowledge proofs that require no trusted setup. They are designed to enable private transactions for cryptocurrencies.

There are already a number of zero-knowledge projects using these technologies, including zk-chain, zkSync, and Loopring.

# Benefits of Zero-Knowledge Proofs

The primary benefit of zero-knowledge proofs is the ability to leverage privacy-preserving datasets within transparent systems such as public blockchain networks like Ethereum. While blockchains are designed to be highly transparent, where anyone running their own blockchain node can see and download all data stored on the ledger, the addition of ZKP technology allows users and businesses alike to leverage their private datasets in the execution of smart contracts without revealing the underlying data.

Ensuring privacy within blockchain networks is crucial to traditional institutions such as supply chain companies, enterprises, and banks that want to interact with and launch smart contracts but need to keep their trade secrets confidential to stay competitive. Additionally, such institutions are often required by law to safeguard their client’s Personally Identifiable Information (PII) and comply with regulations such as the Europe Union’s General Data Protection Regulation (GDPR) and the United States’ Health Insurance Portability and Accountability Act (HIPAA).

While permissioned blockchain networks have emerged as a means of preserving transaction privacy for institutions from the public’s eye, ZKPs allow institutions to securely interact with public blockchain networks — which often benefit from a large network effect of users around the world — without giving up control of sensitive and proprietary datasets. As a result, ZKP technology is successfully opening up a wide range of institutional use cases for public blockchain networks that were previously inaccessible, incentivizing innovation and creating a more efficient global economy.

# Zero-Knowledge Proof Use Cases

Zero-knowledge proofs unlock exciting use cases across Web3, enhancing security, protecting user privacy, and supporting scaling with layer 2s.

## Private Transactions

ZKPs have been used by blockchains such as Zcash to allow users to create privacy-preserving transactions that keep the monetary amount, sender, and receiver addresses private.

## Verifiable Computations

Decentralized oracle networks, which provide smart contracts with access to off-chain data and computation, can also leverage ZKPs to prove some fact about an off-chain data point, without revealing the underlying data on-chain.

## Highly Scalable and Secure Layer 2s

Verifiable computations through methods such as zk-Rollups, Validiums, and Volitions enable highly secure and scalable layer 2s. Using layer 1s such as Binance as a settlement layer, they can provide dApps and users with faster and more efficient transactions.

## Decentralized Identity and Authentication

ZKPs can underpin identity management systems that enable users to validate their identity while protecting their personal information. For example, a ZKP-based identity solution could enable a person to verify that they’re a citizen of a country without having to provide their passport details.