How to Implement Zero-Knowledge Proof in Blockchain Applications

Karolina

30 May 2023
How to Implement Zero-Knowledge Proof in Blockchain Applications

As the importance of security and trust have grown within the blockchain technology sphere, it has become vital to establish strong methods for safeguarding sensitive information and maintaining privacy. Zero-knowledge proof, a mechanism that has attracted considerable interest, allows for the verification of data without exposing the actual content. In this article, we will delve into the effective incorporation of zero-knowledge proof within blockchain applications. We will explain how to implement Zero-Knowledge Proof in Blockchain Application. By comprehending its underlying principles and complexities and adhering to the steps detailed below, businesses can utilize this influential instrument to enhance their blockchain solutions in terms of privacy, integrity, and authentication.

Understanding Zero-Knowledge Proof

Fundamentally, zero-knowledge proof is a cryptographic notion permitting one entity, termed as the prover, to demonstrate the accuracy of a certain claim to another entity, called the verifier, without disclosing any details about the claim itself. Put simply, zero-knowledge proof allows the prover to persuade the verifier of a statement's truth while keeping the relevant data or knowledge hidden. This concept was first put forward by Shafi Goldwasser, Silvio Micali, and Charles Rackoff in 1985 and has since emerged as an indispensable resource in maintaining data privacy and security.

For a zero-knowledge proof to be successful, it requires four main elements. The prover, the verifier, the statement, and the proof. The prover is responsible for establishing the truthfulness of a statement without divulging any actual information. On the other hand, it is up to verifier to confirm that proof offered by prover is accurate without acquiring any knowledge concerning underlying details. Meanwhile, the statement symbolizes what the prover seeks to validate whereas proof embodies evidence supplied by prover in order to persuade verifier regarding validity of said statement.

Why Use Zero-Knowledge Proof in Blockchain?

The blockchain technology, characterized by its decentralized nature, transparency, and immutability, has revolutionized various sectors. However, as much as transparency is a boon in blockchain applications, it can sometimes become a bane when it comes to privacy. This is where the concept of Zero-Knowledge Proof (ZKP) comes into play.

Benefits of Zero-Knowledge Proof in Blockchain

Zero-Knowledge Proofs offer several advantages that make them an attractive choice for enhancing privacy and security in blockchain applications:

  • Enhanced Privacy: ZKP allows users to verify transactions without revealing any additional information beyond the fact that the transaction is valid. This helps protect sensitive information from being publicly accessible on the blockchain.
  • Reduced Fraud: By ensuring that only valid transactions are added to the blockchain, ZKPs can significantly decrease the potential for fraudulent activity.
  • Increased Efficiency: In some scenarios, ZKP can reduce the amount of data that needs to be stored on the blockchain. With ZKP, the proof of a transaction's validity can be much smaller than the transaction data itself.
  • Greater Interoperability: ZKP enables secure interactions between different blockchain systems, facilitating cross-chain transactions and increasing the overall interoperability of the blockchain ecosystem.

Current Applications of Zero-Knowledge Proof in Blockchain 

There are several notable applications currently using Zero-Knowledge Proofs to enhance their operations:

  • Zcash: This cryptocurrency uses ZKP (specifically a variant called zk-SNARKs) to provide its users with the option to hide the sender, receiver, and value of transactions, all while allowing network miners to verify transactions without gaining any knowledge about the specifics.
  • Ethereum: Ethereum has been exploring the integration of ZKP to improve both privacy and scalability. It aims to enable private transactions and to create off-chain transactions that can be verified on-chain.
  • StarkWare: StarkWare uses ZKP (specifically zk-STARKs) to enhance scalability and privacy in various applications, including decentralized exchanges and gaming platforms. The technology enables processing and verification of large amounts of data off-chain, reducing the load on the blockchain itself.

These examples illustrate the diverse uses and potential of Zero-Knowledge Proofs in blockchain applications. The ability to prove and verify transactions without revealing any additional information is a powerful tool that can significantly enhance the privacy, security, and efficiency of blockchain systems.

How to Implement Zero-Knowledge Proof in blockchain applications

The initial step involves a comprehensive grasp of ZKP and its variants such as zk-SNARKs (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) and zk-STARKs (Zero-Knowledge Scalable Transparent ARguments of Knowledge). This involves studying cryptographic principles, mathematical concepts, and computational theories underpinning these proofs.

Read our Ultimate Guide to ZKP: zk-SNARKs vs zk-STARKs

The next phase of 'How to Implement Zero-Knowledge Proof' requires understanding the blockchain platform. This includes knowledge of the platform's architecture, its scripting language, and its privacy and security protocols. The choice of platform may depend on the specific requirements of the application, as different platforms offer varying degrees of support for ZKP.

The actual implementation process begins with defining the private and public inputs for the proof. The private inputs are the data that the prover wants to keep secret. The public inputs are the information that can be openly shared. A 'witness' is then generated, which is a solution to the mathematical problem defined by these inputs.

The next step is the creation of a proving key and a verification key, using a setup algorithm. The proving key generates proofs, and the verification key checks the validity of these proofs. After this, the prover uses the proving key and the witness to create a proof. It asserts that they know a solution to the problem without revealing the solution itself.

Once the proof is generated, it can be verified by anyone using the verification key. This ensures that the proof is valid and that the prover knows the private inputs. All without revealing any additional information.

After the successful verification of the proof, it can be integrated into the blockchain application. This could involve creating transactions that include the proof, or setting up smart contracts that require a valid proof to execute certain functions.

Challenges and Considerations

Incorporating zero-knowledge proof into blockchain applications entails numerous hurdles and deliberations. To capitalize on the advantages of zero-knowledge proof, grasping and alleviating these challenges is vital. Some important aspects to take into account are:

Operational Overhead and Proficiency

Assessing Performance Consequences: The computations in zero-knowledge proof can be demanding, possibly impacting blockchain applications' performance. It is critical to examine the operational overhead induced by the chosen protocol and refine it as much as feasible.

Refinement Approaches: Investigating methods like enhanced algorithms, parallel computation, or assigning calculations to specialized equipment can help alleviate operational overhead and boost efficiency.

Expandability and Compatibility

Tackling Expandability Issues: Zero-knowledge proof protocols might cause challenges in expandability when employed on a massive scale. As the blockchain network expands, both computational necessities and communication intricacies of zero-knowledge proofs can considerably rise. Inspecting expandability solutions, like sharding or layer-two protocols, assists in surmounting these issues.

Compatibility Among Networks: Certifying harmony and compatibility of implementations amidst various blockchain networks is essential for unobstructed collaboration between diverse systems. Contemplate standards and protocols that enable cross-chain interaction to accomplish compatibility.

Security Threats and Confidence Presumptions

Scrutinizing Assumptions and Vulnerabilities. ZKP protocols are founded on distinct assumptions and cryptographic building blocks. Evaluating assumed premises and possible susceptibilities tied to the chosen protocol is imperative. Staying up-to-date with any breakthroughs or latent flaws in the protocol aids in maintaining long-term security.

Supplementary Security Precautions. Although zero-knowledge proofs deliver superior privacy and security, one should not be overly dependent on them. Implementing supplementary safety measures, like secure key administration, encryption, and stringent access control, offers extra levels of safeguarding.

Thorough contemplation of these hurdles and addressing them throughout the implementation stage enables organizations to surmount potential impediments and effectively integrate zero-knowledge proof into their blockchain applications. It is critical to stay current with the newest research and developments in zero-knowledge proof methods to warrant the security, efficacy, and expandability of the executed solution.

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AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

Gracjan Prusik

11 Mar 2025
AI-Driven Frontend Automation: Elevating Developer Productivity to New Heights

AI Revolution in the Frontend Developer's Workshop

In today's world, programming without AI support means giving up a powerful tool that radically increases a developer's productivity and efficiency. For the modern developer, AI in frontend automation is not just a curiosity, but a key tool that enhances productivity. From automatically generating components, to refactoring, and testing – AI tools are fundamentally changing our daily work, allowing us to focus on the creative aspects of programming instead of the tedious task of writing repetitive code. In this article, I will show how these tools are most commonly used to work faster, smarter, and with greater satisfaction.

This post kicks off a series dedicated to the use of AI in frontend automation, where we will analyze and discuss specific tools, techniques, and practical use cases of AI that help developers in their everyday tasks.

AI in Frontend Automation – How It Helps with Code Refactoring

One of the most common uses of AI is improving code quality and finding errors. These tools can analyze code and suggest optimizations. As a result, we will be able to write code much faster and significantly reduce the risk of human error.

How AI Saves Us from Frustrating Bugs

Imagine this situation: you spend hours debugging an application, not understanding why data isn't being fetched. Everything seems correct, the syntax is fine, yet something isn't working. Often, the problem lies in small details that are hard to catch when reviewing the code.

Let’s take a look at an example:

function fetchData() {
    fetch("htts://jsonplaceholder.typicode.com/posts")
      .then((response) => response.json())
      .then((data) => console.log(data))
      .catch((error) => console.error(error));
}

At first glance, the code looks correct. However, upon running it, no data is retrieved. Why? There’s a typo in the URL – "htts" instead of "https." This is a classic example of an error that could cost a developer hours of frustrating debugging.

When we ask AI to refactor this code, not only will we receive a more readable version using newer patterns (async/await), but also – and most importantly – AI will automatically detect and fix the typo in the URL:

async function fetchPosts() {
    try {
      const response = await fetch(
        "https://jsonplaceholder.typicode.com/posts"
      );
      const data = await response.json();
      console.log(data);
    } catch (error) {
      console.error(error);
    }
}

How AI in Frontend Automation Speeds Up UI Creation

One of the most obvious applications of AI in frontend development is generating UI components. Tools like GitHub Copilot, ChatGPT, or Claude can generate component code based on a short description or an image provided to them.

With these tools, we can create complex user interfaces in just a few seconds. Generating a complete, functional UI component often takes less than a minute. Furthermore, the generated code is typically error-free, includes appropriate animations, and is fully responsive, adapting to different screen sizes. It is important to describe exactly what we expect.

Here’s a view generated by Claude after entering the request: “Based on the loaded data, display posts. The page should be responsive. The main colors are: #CCFF89, #151515, and #E4E4E4.”

Generated posts view

AI in Code Analysis and Understanding

AI can analyze existing code and help understand it, which is particularly useful in large, complex projects or code written by someone else.

Example: Generating a summary of a function's behavior

Let’s assume we have a function for processing user data, the workings of which we don’t understand at first glance. AI can analyze the code and generate a readable explanation:

function processUserData(users) {
  return users
    .filter(user => user.isActive) // Checks the `isActive` value for each user and keeps only the objects where `isActive` is true
    .map(user => ({ 
      id: user.id, // Retrieves the `id` value from each user object
      name: `${user.firstName} ${user.lastName}`, // Creates a new string by combining `firstName` and `lastName`
      email: user.email.toLowerCase(), // Converts the email address to lowercase
    }));
}

In this case, AI not only summarizes the code's functionality but also breaks down individual operations into easier-to-understand segments.

AI in Frontend Automation – Translations and Error Detection

Every frontend developer knows that programming isn’t just about creatively building interfaces—it also involves many repetitive, tedious tasks. One of these is implementing translations for multilingual applications (i18n). Adding translations for each key in JSON files and then verifying them can be time-consuming and error-prone.

However, AI can significantly speed up this process. Using ChatGPT, DeepSeek, or Claude allows for automatic generation of translations for the user interface, as well as detecting linguistic and stylistic errors.

Example:

We have a translation file in JSON format:

{
  "welcome_message": "Welcome to our application!",
  "logout_button": "Log out",
  "error_message": "Something went wrong. Please try again later."
}

AI can automatically generate its Polish version:

{
  "welcome_message": "Witaj w naszej aplikacji!",
  "logout_button": "Wyloguj się",
  "error_message": "Coś poszło nie tak. Spróbuj ponownie później."
}

Moreover, AI can detect spelling errors or inconsistencies in translations. For example, if one part of the application uses "Log out" and another says "Exit," AI can suggest unifying the terminology.

This type of automation not only saves time but also minimizes the risk of human errors. And this is just one example – AI also assists in generating documentation, writing tests, and optimizing performance, which we will discuss in upcoming articles.

Summary

Artificial intelligence is transforming the way frontend developers work daily. From generating components and refactoring code to detecting errors, automating testing, and documentation—AI significantly accelerates and streamlines the development process. Without these tools, we would lose a lot of valuable time, which we certainly want to avoid.

In the next parts of this series, we will cover topics such as:

Stay tuned to keep up with the latest insights!

The Ultimate Web3 Backend Guide: Supercharge dApps with APIs

Tomasz Dybowski

04 Mar 2025
The Ultimate Web3 Backend Guide: Supercharge dApps with APIs

Introduction

Web3 backend development is essential for building scalable, efficient and decentralized applications (dApps) on EVM-compatible blockchains like Ethereum, Polygon, and Base. A robust Web3 backend enables off-chain computations, efficient data management and better security, ensuring seamless interaction between smart contracts, databases and frontend applications.

Unlike traditional Web2 applications that rely entirely on centralized servers, Web3 applications aim to minimize reliance on centralized entities. However, full decentralization isn't always possible or practical, especially when it comes to high-performance requirements, user authentication or storing large datasets. A well-structured backend in Web3 ensures that these limitations are addressed, allowing for a seamless user experience while maintaining decentralization where it matters most.

Furthermore, dApps require efficient backend solutions to handle real-time data processing, reduce latency, and provide smooth user interactions. Without a well-integrated backend, users may experience delays in transactions, inconsistencies in data retrieval, and inefficiencies in accessing decentralized services. Consequently, Web3 backend development is a crucial component in ensuring a balance between decentralization, security, and functionality.

This article explores:

  • When and why Web3 dApps need a backend
  • Why not all applications should be fully on-chain
  • Architecture examples of hybrid dApps
  • A comparison between APIs and blockchain-based logic

This post kicks off a Web3 backend development series, where we focus on the technical aspects of implementing Web3 backend solutions for decentralized applications.

Why Do Some Web3 Projects Need a Backend?

Web3 applications seek to achieve decentralization, but real-world constraints often necessitate hybrid architectures that include both on-chain and off-chain components. While decentralized smart contracts provide trustless execution, they come with significant limitations, such as high gas fees, slow transaction finality, and the inability to store large amounts of data. A backend helps address these challenges by handling logic and data management more efficiently while still ensuring that core transactions remain secure and verifiable on-chain.

Moreover, Web3 applications must consider user experience. Fully decentralized applications often struggle with slow transaction speeds, which can negatively impact usability. A hybrid backend allows for pre-processing operations off-chain while committing final results to the blockchain. This ensures that users experience fast and responsive interactions without compromising security and transparency.

While decentralization is a core principle of blockchain technology, many dApps still rely on a Web2-style backend for practical reasons:

1. Performance & Scalability in Web3 Backend Development

  • Smart contracts are expensive to execute and require gas fees for every interaction.
  • Offloading non-essential computations to a backend reduces costs and improves performance.
  • Caching and load balancing mechanisms in traditional backends ensure smooth dApp performance and improve response times for dApp users.
  • Event-driven architectures using tools like Redis or Kafka can help manage asynchronous data processing efficiently.

2. Web3 APIs for Data Storage and Off-Chain Access

  • Storing large amounts of data on-chain is impractical due to high costs.
  • APIs allow dApps to store & fetch off-chain data (e.g. user profiles, transaction history).
  • Decentralized storage solutions like IPFS, Arweave and Filecoin can be used for storing immutable data (e.g. NFT metadata), but a Web2 backend helps with indexing and querying structured data efficiently.

3. Advanced Logic & Data Aggregation in Web3 Backend

  • Some dApps need complex business logic that is inefficient or impossible to implement in a smart contract.
  • Backend APIs allow for data aggregation from multiple sources, including oracles (e.g. Chainlink) and off-chain databases.
  • Middleware solutions like The Graph help in indexing blockchain data efficiently, reducing the need for on-chain computation.

4. User Authentication & Role Management in Web3 dApps

  • Many applications require user logins, permissions or KYC compliance.
  • Blockchain does not natively support session-based authentication, requiring a backend for handling this logic.
  • Tools like Firebase Auth, Auth0 or Web3Auth can be used to integrate seamless authentication for Web3 applications.

5. Cost Optimization with Web3 APIs

  • Every change in a smart contract requires a new audit, costing tens of thousands of dollars.
  • By handling logic off-chain where possible, projects can minimize expensive redeployments.
  • Using layer 2 solutions like Optimism, Arbitrum and zkSync can significantly reduce gas costs.

Web3 Backend Development: Tools and Technologies

A modern Web3 backend integrates multiple tools to handle smart contract interactions, data storage, and security. Understanding these tools is crucial to developing a scalable and efficient backend for dApps. Without the right stack, developers may face inefficiencies, security risks, and scaling challenges that limit the adoption of their Web3 applications.

Unlike traditional backend development, Web3 requires additional considerations, such as decentralized authentication, smart contract integration, and secure data management across both on-chain and off-chain environments.

Here’s an overview of the essential Web3 backend tech stack:

1. API Development for Web3 Backend Services

  • Node.js is the go-to backend runtime good for Web3 applications due to its asynchronous event-driven architecture.
  • NestJS is a framework built on top of Node.js, providing modular architecture and TypeScript support for structured backend development.

2. Smart Contract Interaction Libraries for Web3 Backend

  • Ethers.js and Web3.js are TypeScript/JavaScript libraries used for interacting with Ethereum-compatible blockchains.

3. Database Solutions for Web3 Backend

  • PostgreSQL: Structured database used for storing off-chain transactional data.
  • MongoDB: NoSQL database for flexible schema data storage.
  • Firebase: A set of tools used, among other things, for user authentication.
  • The Graph: Decentralized indexing protocol used to query blockchain data efficiently.

4. Cloud Services and Hosting for Web3 APIs

When It Doesn't Make Sense to Go Fully On-Chain

Decentralization is valuable, but it comes at a cost. Fully on-chain applications suffer from performance limitations, high costs and slow execution speeds. For many use cases, a hybrid Web3 architecture that utilizes a mix of blockchain-based and off-chain components provides a more scalable and cost-effective solution.

In some cases, forcing full decentralization is unnecessary and inefficient. A hybrid Web3 architecture balances decentralization and practicality by allowing non-essential logic and data storage to be handled off-chain while maintaining trustless and verifiable interactions on-chain.

The key challenge when designing a hybrid Web3 backend is ensuring that off-chain computations remain auditable and transparent. This can be achieved through cryptographic proofs, hash commitments and off-chain data attestations that anchor trust into the blockchain while improving efficiency.

For example, Optimistic Rollups and ZK-Rollups allow computations to happen off-chain while only submitting finalized data to Ethereum, reducing fees and increasing throughput. Similarly, state channels enable fast, low-cost transactions that only require occasional settlement on-chain.

A well-balanced Web3 backend architecture ensures that critical dApp functionalities remain decentralized while offloading resource-intensive tasks to off-chain systems. This makes applications cheaper, faster and more user-friendly while still adhering to blockchain's principles of transparency and security.

Example: NFT-based Game with Off-Chain Logic

Imagine a Web3 game where users buy, trade and battle NFT-based characters. While asset ownership should be on-chain, other elements like:

  • Game logic (e.g., matchmaking, leaderboard calculations)
  • User profiles & stats
  • Off-chain notifications

can be handled off-chain to improve speed and cost-effectiveness.

Architecture Diagram

Below is an example diagram showing how a hybrid Web3 application splits responsibilities between backend and blockchain components.

Hybrid Web3 Architecture

Comparing Web3 Backend APIs vs. Blockchain-Based Logic

FeatureWeb3 Backend (API)Blockchain (Smart Contracts)
Change ManagementCan be updated easilyEvery change requires a new contract deployment
CostTraditional hosting feesHigh gas fees + costly audits
Data StorageCan store large datasetsLimited and expensive storage
SecuritySecure but relies on centralized infrastructureFully decentralized & trustless
PerformanceFast response timesLimited by blockchain throughput

Reducing Web3 Costs with AI Smart Contract Audit

One of the biggest pain points in Web3 development is the cost of smart contract audits. Each change to the contract code requires a new audit, often costing tens of thousands of dollars.

To address this issue, Nextrope is developing an AI-powered smart contract auditing tool, which:

  • Reduces audit costs by automating code analysis.
  • Speeds up development cycles by catching vulnerabilities early.
  • Improves security by providing quick feedback.

This AI-powered solution will be a game-changer for the industry, making smart contract development more cost-effective and accessible.

Conclusion

Web3 backend development plays a crucial role in scalable and efficient dApps. While full decentralization is ideal in some cases, many projects benefit from a hybrid architecture, where off-chain components optimize performance, reduce costs and improve user experience.

In future posts in this Web3 backend series, we’ll explore specific implementation details, including:

  • How to design a Web3 API for dApps
  • Best practices for integrating backend services
  • Security challenges and solutions

Stay tuned for the next article in this series!