Zero-knowledge proofs (ZKPs) are a groundbreaking concept in the field of cryptography. They allow one party (the prover) to prove the truth of a statement without revealing sensitive data and are highly valuable in numerous scenarios where privacy preservation is necessary, such as financial transactions, electronic voting, and identity verifications, among others.
ZKPs emerged in the 1980s due to the pioneering work of Shafi Goldwasser, Silvio Micali, and Charles Rackoff. Since then, they have evolved into a highly sophisticated technology.
In this guide, we will delve into everything associated with zero-knowledge proofs, from their history to their multiple use cases. We will explain the pros and cons of this innovative cryptographic method and its growing relevance in the cryptocurrency industry.
Zero-Knowledge Proofs Defined
Zero-Knowledge Proofs (ZKPs) are cryptographic protocols that allow one party to demonstrate to another party that they possess knowledge of certain secret data without revealing the data itself. In essence, they only prove the accuracy of the information without disclosing additional information.
For instance, a ZKP could verify a person’s identity through official documents without requiring the person to provide a copy of those documents. It could also prove knowledge of the solution to a mathematical puzzle without revealing the solution itself.
Therefore, ZKPs are an innovative technology that seeks to protect the client’s privacy while maintaining a balance between transparency, security, and data control.
History of ZKPs
The concept of ZKPs was originally presented in 1985 by Shafi Goldwasser, Silvio Micali, and Charles Rackoff in a paper titled “The knowledge complexity of interactive proof-systems”. They demonstrated the possibility of proving knowledge of a secret without revealing the secret itself through interactions between the prover and the verifier.
As per the paper, “zero-knowledge proofs are defined as those proofs that convey no additional knowledge other than the correctness of the proposition in question.”
In the 1990s, non-interactive ZKPs were developed, wherein the prover generated proof that could be verified by any party without the need for interaction. The most notable non-interactive protocol was the Fiat-Shamir protocol, developed by Amos Fiat and Adi Shamir. It converted interactive zero-knowledge proofs into non-interactive versions, making them more efficient and practical.
The non-interactive Fiat-Shamir protocols were a crucial advancement in the practical adoption of ZKPs, as they helped create zero-knowledge proofs verifiable by parties beyond the original prover and verifier.
In the 2000s, Succinct Non-Interactive Arguments of Knowledge (SNARKs) emerged. This is a type of ZKPs that offer an efficient and compact way to generate and verify cryptographic proofs. They use advanced mathematical problems involving elliptic curves and bilinear pairings.
Between 2016 and 2018, Scalable Transparent Arguments of Knowledge (STARKs) were developed. STARKs are considered an evolution of existing SNARKs. Unlike his predecessor, they don’t require trust setups, enhancing transparency and operability.
How Do Zero-Knowledge Proofs Work?
ZKPs operate through an interactive challenge response between the prover and the verifier. First, the prover generates a cryptographic commitment that conceals the secret data while mathematically linking to it. This commitment can use cryptographic hash functions, for instance.
Subsequently, the verifier sends a random challenge to the prover, who must respond with proof demonstrating knowledge of the information, without directly exposing it. If the response is correct, the verifier is convinced that the prover possesses the knowledge, yet the information remains undisclosed.
This is a detailed step-by-step explanation of the functioning of ZKPs:
- The prover generates a cryptographic commitment that conceals the secret data while mathematically linking to it. For instance, cryptographic hash functions can be used.
- The verifier issues a “challenge”, requesting the prover to demonstrate a certain property of the secret data like it falling within a specific range.
- The prover employs the secret data to generate a “response” satisfying the property requested by the challenge. For instance, proving that a hash of the data falls within a certain range.
- The verifier validates that the response meets the challenge, without accessing the secret data itself. It only confirms the property.
- Steps 2 to 4 are repeated multiple times with random challenges to minimize the risk of deception.
- After several rounds of interaction, the verifier gains statistical certainty about the validity of the assertion, without the secret data being exposed.
An example in the banking industry is when a customer wants to prove their identity to a bank without revealing sensitive personal data like their social security number. In this case, a ZKP known as a zero-knowledge proof of identity (ZKPI) can be employed. The customer (prover) provides proof to the bank (verifier), such as a government-issued ID, allowing the bank to confirm the customer’s identity without obtaining any sensitive information.
Zero Knowledge vs Zero Trust
The term “zero knowledge” refers specifically to cryptographic proofs that allow verification of information without exposing the underlying data. It enables privacy preservation during validation and authentication processes.
On the other hand, “zero trust” is a broader cybersecurity approach that operates under the assumption that no user or device should inherently be trusted within a system. The zero trust model requires continuous authentication and authorization for granting access to resources.
The goal of zero trust is to minimize internal threats like data leaks, ransomware infections, and other malicious activities. It casts a wider net of ongoing scrutiny across all interactions.
In summary, zero-knowledge proofs provide privacy by revealing only the validity of information, whereas zero trust maximizes security by removing implicit trust assumptions.
ZKPs’ Use Cases
Several major use cases of ZKPs include:
- Anonymous payments. Cryptocurrencies like Zcash utilize ZKPs to conceal transaction details.
- Decentralized identity systems. ZKPs enable identity verification while preserving user privacy.
- Authentication. Users can authenticate their identity without revealing passwords or personal data.
- Electronic voting. Vote counts can be verified without exposing individual votes.
- Result validation. Blockchains like Ethereum can validate off-chain computation results through ZKPs.
- Finance. Institutions can verify private customer data without accessing sensitive or private information.
Pros & Cons of ZKPs
Key advantages of ZKPs include improved privacy by concealing sensitive data, enhanced cryptographic security during data verification and authentication, opportunities for previously impossible use cases due to new privacy requirements, and models like STARKs that eliminate the need for third-party trust.
Meanwhile, there are also potential drawbacks. Firstly, zero-knowledge proofs are generating and verifying proofs can be computationally expensive. Secondly, specialized hardware is required, raising implementation costs. Further, auditing and debugging can be more challenging, increasing vulnerability risks. And finally, some designs like SNARKs still rely on trust setups.
Zero-knowledge proofs are among the most significant cryptographic advancements in recent decades. They allow the validation of claims and credentials without exposing underlying sensitive data.
Hence, despite challenges in terms of efficiency and trust, zero-knowledge proofs are expected to gain broader adoption in the coming years, providing users with greater control over their data.