Introduction to Zero-Knowledge Proofs (ZKPs)
Are you tired of sharing your personal information online and exposing yourself to potential data breaches and identity theft? Well, there’s a solution for that – zero-knowledge proofs (ZKPs).
According to Vitalik Buterin, the co-founder of Ethereum, ZKPs are “one of the most promising technologies in the blockchain space.” They allow you to prove that you have certain information or knowledge, without actually revealing that information or knowledge.
In other words, ZKPs enable you to prove to someone else that you know a secret, without revealing the secret itself. This makes them incredibly powerful for privacy-preserving transactions and secure authentication, among other use cases.
To get a better understanding of how ZKPs work, you can check out this episode of the Epicenter podcast, where host Sunny Aggarwal interviews Eli Ben-Sasson, a leading expert in zero-knowledge proofs. You can also watch this video by web3 thought leader Andreas Antonopoulos, where he explains how ZKPs can be used to protect privacy in decentralized systems.
ZKPs come in different types, including non-interactive zero-knowledge proofs (NIZKs), interactive zero-knowledge proofs (IZKs), zk-SNARKs, zk-STARKs, and Bulletproofs. We will be diving deeper into these types in this post.
Now that you have a basic understanding of what zero-knowledge proofs are and why they’re important, let’s dive deeper into how they work.
Types of Zero-Knowledge Proofs
There are several types of zero-knowledge proofs, each with their own unique properties and uses. Here are some of the most common types:
Interactive proofs: These are the OG of zero-knowledge proofs and involve a prover and a verifier interacting with each other to prove the prover’s knowledge. Think of it like a game of “20 Questions”, but with cryptography. Interactive proofs are great for basic use cases, but can be time-consuming and inefficient.
Non-Interactive Proofs: These are like the introverted cousin of interactive proofs, and involve a prover creating a proof without interacting with the verifier. This is useful when the prover and verifier can’t communicate directly or when the prover wants to keep their identity private. Non-interactive proofs are like the ninja of ZKPs, silently proving knowledge without anyone even noticing.
Succinct Non-Interactive Arguments of Knowledge (SNARKs): Now, we’re getting into the heavy hitters. SNARKs are a type of ZKP that are particularly efficient and can be used to prove the authenticity of large amounts of data without revealing the actual data itself. They’re like the bouncers at the club, verifying the authenticity of the VIPs without revealing their identities.
Scalable Transparent Arguments of Knowledge (STARKs): These are like the punk rock rebels of ZKPs. STARKs don’t require a trusted setup, which means they can be used in a wide range of applications without the need for a central authority. STARKs have been proposed as a solution for various problems, including private transactions on blockchain networks and secure multi-party computation.
Proofs of Knowledge: These are like the “I know a secret” ZKPs. They’re used to prove that a prover knows a particular secret without revealing it itself. This is great for authentication protocols, where users must prove that they know a password or other secret to access a system. Proofs of knowledge are like the whisperers of ZKPs, proving knowledge without anyone else hearing the secret.
I will write a separate article explaining these types in more detail. This much is sufficient to claim that “I am aware of what ZKPs are.”
How Zero-Knowledge Proofs Work
Zero-knowledge proofs (ZKPs) are a powerful tool for proving the validity of a statement without revealing any additional information beyond what’s necessary. ZKPs work by using complex mathematical algorithms that allow a prover to convince a verifier that they know something without revealing what that knowledge actually is.
At a high level, ZKPs work by dividing the proof process into two parts: a commitment phase and a challenge phase. In the commitment phase, the prover generates a commitment to their secret knowledge that can be verified later in the challenge phase. In the challenge phase, the verifier chooses a random challenge that the prover must respond to using their secret knowledge. If the prover is able to respond correctly, the verifier can be confident that the prover has knowledge of the secret without actually learning the secret itself.
To give a concrete example, let’s return to Alice and Bob. Suppose Alice wants to prove to Bob that she knows the solution to a difficult math problem without revealing the solution itself. Here’s how they might use a ZKP to accomplish this:
- Alice and Bob agree on a set of rules for the zero-knowledge proof. These rules might include the specific information that Alice needs to prove (in this case, her knowledge of the solution to the math problem) and the specific actions that Alice needs to take to prove her knowledge (for instance, writing down a sequence of numbers that demonstrates her knowledge of the solution).
- In the commitment phase, Alice generates a commitment to her knowledge of the solution. This commitment might involve encrypting her knowledge using a one-way function that can be verified later.
- In the challenge phase, Bob chooses a random challenge that Alice must respond to using her secret knowledge. For example, Bob might ask Alice to provide the 10th number in the sequence she wrote down earlier.
- If Alice is able to respond correctly, Bob can be confident that she has knowledge of the solution to the math problem without actually learning the solution itself.
Understanding the role of randomness and commitments
To understand the role of randomness and commitments in zero-knowledge proofs, let’s take a closer look at how they work.
In a zero-knowledge proof, the prover aims to convince the verifier that they know a certain piece of information without actually revealing that information. To accomplish this, the prover uses a combination of randomness and commitments.
Randomness is important in zero-knowledge proofs because it helps to prevent cheating. If the prover and verifier agree on a set of rules for the proof, the prover could theoretically try to brute-force their way through those rules to prove their knowledge without actually possessing it. However, if the rules involve randomness, it becomes much harder to cheat the system.
Commitments are another key ingredient in zero-knowledge proofs. A commitment is a way for the prover to “lock in” their knowledge of the secret without revealing it. This is done by creating a commitment that includes the secret and a random value, which is then shared with the verifier. Later on, the prover can reveal the secret and the random value to the verifier, who can check that the commitment matches the revealed values. This ensures that the prover knew the secret all along without revealing it until the final reveal.
Applications of Zero-Knowledge Proofs in Web3 Technology
The use of zero-knowledge proofs in Web3 technology is widespread. Examples of how ZKPs are typically used on the Web3 are as follows:
Privacy-preserving transactions
To ensure that users and their transaction data remain private, ZKPs can be used to enable anonymous and confidential transactions on blockchain networks. As an example, Zcash uses ZK-SNARKs to ensure the privacy of transactions on its blockchain.
Identity verification
Zero-knowledge proofs (ZKPs) can be used to confirm a user’s identity without requesting sensitive information. This is especially helpful in distributed systems, where there is no centralized identity verification service. The Sovrin Network, for instance, uses ZKPs to facilitate distributed identity verification.
Data sharing and verification
ZKPs can be used to make data sharing and verification on blockchain networks private and secure. It is now possible to share sensitive information while maintaining privacy thanks to ZKPs, which can be used, for instance, to verify the authenticity of data without revealing the data itself.
Proof of ownership
Zero-knowledge proofs (ZKPs) can be used to verify possession of physical or digital property without disclosing the owner’s personal details. Proof of ownership of a cryptocurrency or other digital asset can be established with a ZKP without disclosing the asset’s associated public address.
Access control
ZKPs can be used to implement private and secure access control in distributed environments. If you want to provide someone with access to something without having to reveal their identity, a zero-knowledge proof could be the way to go.
These are only a handful of the many uses of ZKPs in web3. We can expect to see even more creative applications of ZKPs in the future as blockchain-based systems continue to develop and mature.
Problems and Constraints of Zero-Knowledge Proves
While zero-knowledge proofs have many applications, there are also some restrictions and difficulties to be aware of. Here are a few examples:
Computational complexity:
Generation and verification of zero-knowledge proofs can be computationally and time-consuming. This makes them less suitable for uses that demand real-time processing or low-latency responses.
Complexity of implementation:
The implementation of zero-knowledge proofs can be difficult for those who are unfamiliar with the underlying mathematics. Unfortunately, this can make it challenging for programmers to implement zero-knowledge proofs in their applications.
Limited applicability
While zero-knowledge proofs can be used in a wide range of applications, there are some areas where they are not well-suited. For example, some experts argue that zero-knowledge proofs may not be the best solution for privacy in social media, as the complexity of generating and verifying proofs could make it difficult to implement at scale.
Security assumptions
All Web3 protocols, including zero-knowledge proofs, are vulnerable to attacks because they rely on unproven security assumptions. To guarantee the safety of their zero-knowledge proof implementations, developers must be well-versed in the most recent findings and best practices.
Despite these obstacles, zero-knowledge proofs continue to be a useful tool for web3 protocols, and it’s likely that we’ll see even more creative uses of the technology as it develops in the years to come.
Exactly what is ZKEVM?
“ZKEVM” refers to the Zero-Knowledge Execution Environment built for the Ethereum Virtual Machine. Simply put, it’s a system that combines the privacy of zero-knowledge proofs with the versatility of the Ethereum blockchain.
ZKEVM is an Ethereum virtual machine that provides an extra layer of security and anonymity for smart contracts. ZKEVM allows code to be executed on the Ethereum network without disclosing any of the data or calculations performed.
In order to ensure the correctness of a computation, the Ethereum Virtual Machine (EVM) uses zero-knowledge proofs, which prevent it from viewing the input data or intermediate calculations. Developers and users of decentralized applications now have access to improved privacy and security thanks to this.
In upcoming posts, let’s explore ZKEVM in detail.
To sum up, zero-knowledge proofs are a fascinating and potent tool for achieving privacy and security in a variety of contexts. ZKPs have the potential to revolutionize the way we interact with technology across a wide range of applications, from blockchain to authentication. \
Contact us at AuroBlocks, where we specialize in web3 development and cutting-edge technology solutions, if you’re curious about the feasibility of zero-knowledge proofs for your own projects.