What Is a Zero-Knowledge Proof (ZKP)?

While the built-in foil of blockchains provides an advantage in many situations, there are besides a number of smart contract use cases that require privacy due to diverse business or legal reasons such as using proprietorship data as inputs to trigger a smart compress ’ sulfur execution. An increasingly common way privacy is achieved on public blockchain networks is through Zero-Knowledge Proofs (ZKPs) —a method acting for one party to cryptographically prove to another that they possess knowledge about a piece of information without revealing the actual underlie information. In the context of blockchain networks, the entirely information revealed on-chain by a ZKP is that some piece of shroud information is valid and known by the prover with a high degree of certainty .
In this article, we explore how zero-knowledge proof work to provide privacy guarantees, the congress of racial equality benefits they offer to users, and an array of blockchain practice cases that leverage ZKPs. In addition, we showcase how Chainlink ’ s DECO technology allows for the creation of privacy-preserving oracle networks that can prove data came from a specific web server in a confidential and backwards compatible manner .

How a Zero-Knowledge Proof Works

Zero-Knowledge Proofs were first described in a 1985 MIT composition from Shafi Goldwasser and Silvio Micali called “ The Knowledge Complexity of Interactive Proof-Systems ”. In this newspaper, the authors demonstrate that it is possible for a prover to convince a verifier that a specific statement about a data decimal point is genuine without disclosing any extra information about the data. ZKPs can either be interactive—where a prover convinces a particular voucher but needs to repeat this process for each person verifier—or non-interactive—where a prover generates a validation that can be verified by anyone using the lapp proof. additionally, there are now diverse implementations of ZKPs including zk-SNARKS, zk-STARKS, PLONK, and Bulletproofs, with each having their own trade-offs of validation size, prover prison term, confirmation time, and more .
The three fundamental characteristics that define a ZKP include :

  • Completeness: If a instruction is true, then an honest voucher 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 voucher that they possess knowledge about the correct input signal .
  • Zero-knowledge: If the department of state is true, then the voucher learns nothing more from the prover other than the statement is true .

At a high level, the creation of a ZKP involves a voucher asking the prover to perform a series of actions that can only be performed accurately if the prover knows the underlie information. If the prover is entirely guessing as to the solution of these actions, then they will finally be proven incorrectly by the voucher ’ s test with a high degree of probability.

Zero-Knowledge Proof conceptual example
A conceptual case 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 get in touch 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 capture 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 binding, the lone direction to complete the puzzle is for Alice to have cognition of the passcode for the lock door. This work can be repeated multiple times to prove Alice has cognition of the door ’ sulfur passcode and did not happen to choose the good path to take initially with a high degree of probability .
After this action is completed, Bob has a high academic degree of confidence that Alice knows the door ’ randomness passcode without revealing the passcode to Bob. While only a conceptual exercise, ZKPs deploy this same scheme but using cryptography to prove cognition about a data orient without revealing the data degree. With this cave case, there is an input, a path, and an output. In computing there are like systems, circuits, which take some input, pass the remark bespeak through a path of electrical gates and generate an output. Zero-Knowledge Proofs leverage circuits like these to prove statements .
Imagine a computational racing circuit that outputs a value on a curve, for a given stimulation. If a drug user is able to systematically provide the compensate answer to a point on the bend, one can be assured the drug user possesses some cognition about the wind since it becomes increasingly improbable to guess the chastise suffice with each consecutive challenge round off. One can think of the circuit like the way that Alice walks in the cave, if she is able to traverse the circuit with her input signal, she proves she holds some cognition, the “ passcode ” to the circuit, with a high degree of probability. Being able to prove cognition about a data detail without revealing any extra information besides cognition of data provides a number of key benefits, specially within the context of blockchain networks.

Benefits of Zero-Knowledge Proofs

The chief 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 crystalline, where anyone running their own blockchain node can see and download all data stored on the daybook, the summation of ZKP engineering allows users and businesses alike to leverage their individual datasets in the performance of smart contracts without revealing the underlying data .
Ensuring privacy within blockchain networks is crucial to traditional institutions such as provision chain companies, enterprises, and banks that want to interact with and establish 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 mean of preserving transaction privacy for institutions from the public ’ randomness eye, ZKPs allows institutions to securely interact with public blockchain networks—which much benefit from a bombastic net impression of users around the world—without giving up control of sensitive and proprietorship datasets. As a resultant role, ZKP engineering is successfully opening up a wide range of institutional consumption cases for public blockchain networks that were previously inaccessible, incentivizing invention and creating a more effective global economy .

Zero-Knowledge Proof Applications

ZKPs have been used by blockchains such as Zcash to allow users to create privacy-preserving transactions that keep the monetary amount, sender, and liquidator addresses private. Decentralized oracle networks, which provide smart contracts with access to off-chain data and calculation, can besides leverage ZKPs to prove some fact about an off-chain data period, without revealing the underlying data on-chain .
An execution of a Zero-Knowledge Proof-based oracle solution in development is DECO, a privacy-preserving prophet protocol within the Chainlink Network ’ s suite of secure off-chain computations. By extending HTTPS/TLS, the most common protocol used to transfer data over the Internet, DECO guarantees that datum remains individual and tamper-proof during its delivery from diverse private and premium data sources. DECO works with modern TLS versions, requires no hope hardware, and operates in a backwards-compatible manner requiring no server-side modifications. As a solution, DECO-enabled Chainlink oracle nodes can prove facts about data sourced from trusted servers without revealing the datum on-chain, while besides proving the source of the data since the TLS chain of detention is maintained .
With ZKPs like DECO, a wide-eyed range of smart narrow use cases are made possible including undercollateralized loans, where borrowers generate high-assurance credentials attesting to their creditworthiness in a privacy-preserving manner. specifically, borrowers can generate these credentials based on records from authoritative on-line sources, such as established institutions, without exposing potentially medium data such as their name, localization, or exact credit score value ( entirely that it exceeds a predefined brink ) .
DECO can besides be used to power the creation of decentralized identity ( DID ) protocols such as CanDID, where users can obtain and manage their own credentials, quite than relying on a centralized third gear party. such credentials are signed by entities called issuers that can authoritatively associate claims with users such as citizenship, occupation, college degrees, and more. DECO allows any existing web server to become an issuer and provides key-sharing management to back up accounts, ampere well as a privacy-preserving shape of Sybil underground based on definitive unique identifiers such as Social Security Numbers ( SSNs ).

last, ZKP solutions like DECO benefit not only the users, but besides enable traditional institutions and data providers to monetize their proprietorship and sensible datasets in a confidential manner. alternatively of posting the data directly on-chain, only attestations derived from ZKPs proving facts about the data need to be published. This opens up new markets for data providers, who can monetize existing datasets and increase their tax income while ensuring zero data escape. When combined with Chainlink Mixicles, privacy is extended beyond the input data executing an agreement to besides include the terms of the agreement itself .
By combining the inherently guileless nature of blockchain networks with the privacy-preserving design of Zero-Knowledge Proofs, enterprises and institutions benefit from the best of both worlds : they can keep their internal datasets private while still leveraging them in the authentic execution environments of chic sign applications .
Read the Chainlink 2.0 Whitepaper for a deep prima donna into the character of decentralized oracle networks in confidentiality-preserving chic contract systems and sign up for the official Chainlink newsletter for the latest updates about the Chainlink Network .

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