Zero-Knowledge Proofs: How it Works & Use Cases in 2024

Zero-knowledge proofs (ZKPs) are a powerful cryptographic technique that allows one party (the prover) to prove to another party (the verifier) that a certain statement is true without conveying any information apart from the fact that the statement is indeed true.

In today‘s data-driven world, ZKPs hold great promise as a privacy-enhancing technology that can help organizations derive insights from data while protecting sensitive information. This comprehensive guide will take a deep dive into how ZKPs work, the different types and implementations, real-world applications, challenges, latest advancements and more. We aim to provide software engineers, cryptographers and technology executives with a thorough understanding of this rapidly evolving cryptographic protocol.

The rising need for data privacy

Businesses today heavily rely on collecting and mining large amounts of customer data to optimize their products, personalize recommendations, target advertising and more. However, massive data collection also exposes companies to cyberattacks and data breaches.

According to IBM‘s 2022 Cost of a Data Breach report, the average cost of a data breach has risen to $4.35 million globally, a 13% increase from 2021. The average cost per compromised record also went up to $170.

Average cost of data breach per record

Table 1 shows how different factors impacts the cost of a data breach. Companies suffering from lost business due to damaged reputation face almost 4x higher costs.

Impact FactorAvg CostIncrease %
Lost Business$5.89 million92%
Abnormal Churn$4.65 million45%
Brand Reputation Damage$4.50 million51%

Table 1: Factors that increase cost of data breaches. Source: IBM

Apart from financial consequences, data breaches seriously erode customer trust and tarnish brand reputation as shown in Figure 1.

Effects of data breaches

Figure 1: How data breaches impact brand reputation and customer trust. Source: Statista

As per a 2021 McKinsey survey, 63% of customers said they would likely switch providers/brands after a data breach. This highlights the need for stronger data privacy measures like zero-knowledge proofs.

What are zero-knowledge proofs?

The concept of zero-knowledge proofs was first proposed in 1989 by MIT researchers Shafi Goldwasser, Silvio Micali and Charles Rackoff in a paper titled "The Knowledge Complexity of Interactive Proof Systems".

As the name suggests, a zero-knowledge proof allows one party (the prover P) to prove to another party (the verifier V) that a certain statement is true without conveying any additional information apart from the fact that the statement is true.

Let‘s take a simple example to understand this better:

Suppose P wants to prove to V that they know the password to a bank account without revealing the actual password. P can use a ZKP scheme where they will perform some cryptographic operation on the password and present the result to V.

While V will not get to know the password, they can verify through the cryptographic proofs that P indeed knows the valid password. So P successfully proves their knowledge of the password in zero-knowledge!

How do zero-knowledge proofs work?

At a high level, a ZKP works through an interactive protocol involving the generation of a mathematical proof that the prover knows a secret without revealing the secret itself.

There are two standard models for ZKP protocols:

Interactive protocols

This class of protocols involves multiple rounds of interaction between P and V. Here‘s a typical flow:

  1. Commitment – P commits to a certain set of values (e.g. by encrypting them) and sends the commitment to V.
  2. Challenge – V issues a random challenge to P.
  3. Response – P generates a response to the challenge without opening the original commitment.
  4. Verification – V verifies the response using cryptographic techniques to check that it is valid for the commitment made by P.

These interactive rounds are repeated multiple times to reduce the probability that P managed to fool V to a negligible level.

A classic analogy to understand interactive ZKPs is the cave of Ali Baba story involving colored balls proposed by cryptography expert Jean-Jacques Quisquater:

"Alice has two balls, one red and one green, hidden in two boxes. She asks Bob to close his eyes. Then she puts one ball in one box, and the other ball in the other box. She then asks Bob to open his eyes. Bob doesn‘t know which color is in which box. Alice now asks Bob to point at one box. She then opens that box and shows Bob the color of the ball. She then asks Bob if he wants to change his mind about the contents of the other box. Bob says no. Alice has just proved to Bob that she can tell the color of both balls even though the boxes were closed without revealing which ball is in which box."

This interactiveness where Bob can query multiple times is what establishes zero-knowledge.

Non-interactive protocols

Proposed later, these schemes do not require back and forth communication. NIZKPs rely on mathematical constructs that ensure soundness, completeness and zero-knowledge in a single transcript sent from P to V.

However, they require stronger setup assumptions and greater computational power. Let‘s compare some prominent interactive and non-interactive ZKP implementations.

ProtocolTypeFeaturesUsed in
zk-SNARKNIZKPSuccinct, scalable, transparent setupZcash, Ethereum
zk-STARKNIZKPNo trusted setup requiredEthereum, Starkware
BulletproofsInteractiveEfficient range proofsMonero, Mimblewimble

While zk-SNARKs gained popularity earlier, their requirement of a trusted setup raised security concerns. zk-STARKs attempt to solve this but are slower. Research is ongoing to improve efficiency of both categories.

Properties of zero-knowledge proofs

An ideal ZKP scheme has the following properties:

Completeness – If the statement is true, an honest verifier will be convinced of this fact by an honest prover.

Soundness – If the statement is false, no cheating prover can convince an honest verifier that it is true, except with some small probability.

Zero-knowledge – No verifier learns anything other than the fact that the statement is true.

Succinctness – The proof is short and easy to verify. For wide adoption, computational efficiency is critical.

Applications and use cases

Thanks to their special properties, ZKPs are a powerful privacy-preserving technique with many applications:

Blockchain – Public blockchains allow anyone to verify transactions but lack privacy. ZKPs can enable private transactions on blockchains like Ethereum and Monero.

Authentication – ZKPs can enable authentication without exposing passwords or biometrics to the servers.

e-Voting – Voters can prove they correctly voted without revealing who they voted for. ZKPs prevent vote tampering or selling.

Confidential transactions – Banks can use ZKPs to validate transactions like loans or trades without accessing sensitive details.

DRM – Content creators can provide protected media that users can access by proving they have a license.

Machine Learning – ML model owners can charge to run predictions while keeping their model secret.

Some other emerging areas where research is ongoing include decentralized finance (DeFi), Internet of Things (IoT), legal systems and gaming.

Challenges and limitations

While ZKPs are gaining traction, there are still barriers to large scale adoption:

  • Computational complexity – The cryptographic computations required are expensive and slow especially on low powered devices.
  • Not foolproof – There is a tiny probability that a false proof will get verified, though negligible.
  • User experience – Abstract math concepts are hard to grasp for average users. The UX needs to be improved.
  • Standardization – Lack of standards and interoperability between different ZKP implementations.

To drive mainstream adoption, protocols and implementations need to become more efficient, secure and easy to integrate. Active areas of research include scalability, transparent setup models and post-quantum security.

The road ahead

ZKPs are one of the most promising emerging privacy technologies today. Here are some developments to watch out for:

  • Ecosystem growth – Projects like ZKProof aim to grow the ZKP ecosystem through standards, libraries and tools.
  • Language research – Domain-specific languages like ZK-Boo make writing ZKPs easier for developers.
  • Layer 2 solutions – ZKPs help scale blockchain networks through Layer 2 rollup solutions like zkSync and StarkNet.
  • Post-quantum ZK -Making ZK protocols quantum-resistant is vital for long term security.
  • Real-world adoption – Zcash, ING bank and voting system trials demonstrate ZKP benefits. Wider adoption expected.

Gartner estimates that by 2023, 30% of organizations will use privacy-enhancing computation techniques like ZKPs in analytics, up from 5% in 2020. As data security needs grow, zero-knowledge proofs present an exciting way to balance privacy and functionality.


Zero-knowledge proofs enable mathematically verifying the truth of a statement while revealing nothing else. As threats to data privacy increase, ZKPs provide a robust cryptographic technique to preserve confidentiality and drive trusted collaborations.

However, there are still barriers around efficiency and user experience that need to be overcome before we see mass adoption. Extensive research efforts underway are helping make ZK protocols more scalable and real-world use cases are proving their viability. Overall, the future looks promising for this revolutionary concept that offers stronger data privacy!

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