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 in Real Estate: How Does It Support the Housing Market?

Miłosz Mach

18 Mar 2025
AI in Real Estate: How Does It Support the Housing Market?

The digital transformation is reshaping numerous sectors of the economy, and real estate is no exception. By 2025, AI will no longer be a mere gadget but a powerful tool that facilitates customer interactions, streamlines decision-making processes, and optimizes sales operations. Simultaneously, blockchain technology ensures security, transparency, and scalability in transactions. With this article, we launch a series of publications exploring AI in business, focusing today on the application of artificial intelligence within the real estate industry.

AI vs. Tradition: Key Implementations of AI in Real Estate

Designing, selling, and managing properties—traditional methods are increasingly giving way to data-driven decision-making.

Breakthroughs in Customer Service

AI-powered chatbots and virtual assistants are revolutionizing how companies interact with their customers. These tools handle hundreds of inquiries simultaneously, personalize offers, and guide clients through the purchasing process. Implementing AI agents can lead to higher-quality leads for developers and automate responses to most standard customer queries. However, technical challenges in deploying such systems include:

  • Integration with existing real estate databases: Chatbots must have access to up-to-date listings, prices, and availability.
  • Personalization of communication: Systems must adapt their interactions to individual customer needs.
  • Management of industry-specific knowledge: Chatbots require specialized expertise about local real estate markets.

Advanced Data Analysis

Cognitive AI systems utilize deep learning to analyze complex relationships within the real estate market, such as macroeconomic trends, local zoning plans, and user behavior on social media platforms. Deploying such solutions necessitates:

  • Collecting high-quality historical data.
  • Building infrastructure for real-time data processing.
  • Developing appropriate machine learning models.
  • Continuously monitoring and updating models based on new data.

Intelligent Design

Generative artificial intelligence is revolutionizing architectural design. These advanced algorithms can produce dozens of building design variants that account for site constraints, legal requirements, energy efficiency considerations, and aesthetic preferences.

Optimizing Building Energy Efficiency

Smart building management systems (BMS) leverage AI to optimize energy consumption while maintaining resident comfort. Reinforcement learning algorithms analyze data from temperature, humidity, and air quality sensors to adjust heating, cooling, and ventilation parameters effectively.

Integration of AI with Blockchain in Real Estate

The convergence of AI with blockchain technology opens up new possibilities for the real estate sector. Blockchain is a distributed database where information is stored in immutable "blocks." It ensures transaction security and data transparency while AI analyzes these data points to derive actionable insights. In practice, this means that ownership histories, all transactions, and property modifications are recorded in an unalterable format, with AI aiding in interpreting these records and informing decision-making processes.

AI has the potential to bring significant value to the real estate sector—estimated between $110 billion and $180 billion by experts at McKinsey & Company.

Key development directions over the coming years include:

  • Autonomous negotiation systems: AI agents equipped with game theory strategies capable of conducting complex negotiations.
  • AI in urban planning: Algorithms designed to plan city development and optimize spatial allocation.
  • Property tokenization: Leveraging blockchain technology to divide properties into digital tokens that enable fractional investment opportunities.

Conclusion

For companies today, the question is no longer "if" but "how" to implement AI to maximize benefits and enhance competitiveness. A strategic approach begins with identifying specific business challenges followed by selecting appropriate technologies.

What values could AI potentially bring to your organization?
  • Reduction of operational costs through automation
  • Enhanced customer experience and shorter transaction times
  • Increased accuracy in forecasts and valuations, minimizing business risks
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Want to implement AI in your real estate business?

Nextrope specializes in implementing AI and blockchain solutions tailored to specific business needs. Our expertise allows us to:

  • Create intelligent chatbots that serve customers 24/7
  • Implement analytical systems for property valuation
  • Build secure blockchain solutions for real estate transactions
Schedule a free consultation

Or check out other articles from the "AI in Business" series

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!