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Addressing the Quantum Threat: Post Quantum Cryptography in Blockchain

Karolina

07 Jun 2023
Addressing the Quantum Threat: Post Quantum Cryptography in Blockchain

In today's increasingly digital world, the demand for secure and dependable cryptographic systems is at an all-time high. Blockchain technology has emerged as a revolutionary force in many industries, thanks to its decentralized and unchangeable characteristics. However, existing cryptographic algorithms face significant security threats from the advancing quantum computer technology. This article will discuss the significance of post-quantum cryptography in protecting blockchain networks against the impending quantum challenges.

Understanding the Quantum Threat

Quantum computers, employing quantum mechanics principles, promise unprecedented computational capabilities that may render existing cryptographic algorithms ineffective. Conventional encryption techniques, such as RSA and ECC (Elliptic Curve Cryptography), depend on the complexity of specific mathematical problems for security. Quantum computers, however, hold the potential to solve these problems exponentially faster, consequently dismantling the cryptographic foundation that supports blockchain networks.

Various risks are associated with quantum computers' impact on blockchain networks. The most prominent risk includes compromising the security of digital assets managed within blockchain systems. Transactions, smart contracts, and private keys that depend on cryptographic algorithms might become susceptible to quantum computer attacks. As quantum technology progresses, adversaries may decrypt encrypted information, tamper with transactions or counterfeit digital signatures – leading to severe financial and reputational damage for those relying on blockchain networks.

Additionally, blockchain's decentralized and transparent nature makes it particularly prone to quantum attacks. Given that blockchain transactions are publicly accessible, a quantum computer-equipped attacker could retroactively decrypt past transactions. This undermines the core principles of immutability and trust that underpin blockchain technology.

To address this urgent and critical challenge posed by the quantum threat, it's vital to take a proactive approach. Incorporating post-quantum cryptography into blockchain systems is crucial for maintaining long-term security and sustainability of these networks. By utilizing cryptographic algorithms that can withstand quantum computer attacks, blockchain networks can preserve data confidentiality, integrity, and the authenticity of transactions and digital assets. Even in light of quantum advancements.

The subsequent sections of this article will investigate the practicality of implementing post-quantum cryptography in blockchain systems. We will explore specific solutions, evaluate their performance implications, and emphasize the initiatives being taken towards standardization and compatibility. Through this examination, we seek to contribute to the comprehensive understanding and adoption of post-quantum cryptography as a vital defense against the quantum threat within the blockchain environment.

Foto: Eric Lukero/Google

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Exploring the Viability of Post-Quantum Cryptography in Blockchain

Implementing post-quantum cryptography within blockchain systems is a multifaceted effort demanding a thorough examination of numerous aspects. With the impending emergence of quantum computers, shifting to post-quantum cryptographic algorithms entails its own set of challenges. This section delves into the practicality of incorporating post-quantum cryptography into blockchain and scrutinizes the advancements in this domain.

Investigations and Progress in Algorithms

Intensive investigations are being undertaken by cryptographic researchers and organizations to explore post-quantum cryptographic algorithms that can withstand attacks from quantum computers. Lattice-based, code-based, and multivariate-based schemes are some examples that aim to preserve security even against quantum adversaries. Meticulous research and evaluations are performed to assess the mathematical underpinnings, security attributes, and practicality of these algorithms for actual implementation.

Concerns about Performance

A significant hurdle while adopting post-quantum cryptography in blockchain lies in the performance costs arising from these novel algorithms. Frequently, post-quantum cryptographic algorithms demand higher computational power and memory compared to conventional cryptographic algorithms. Such heightened computational requirements can influence blockchain networks' efficiency and scalability, possibly altering transaction throughput and consensus mechanisms. Nevertheless, ongoing investigations and optimization endeavors seek to address these performance issues, making post-quantum cryptography more practical for blockchain systems.

Integration with Current Blockchain Protocols

Modifications and revisions to existing protocols may be essential for integrating post-quantum cryptography into blockchain networks. Blockchain platforms like Ethereum proactively investigate incorporating post-quantum cryptographic algorithms through initiatives such as EIP-2938. The objectives include ensuring congruity and consensus among network users while establishing a trajectory towards quantum-resistant security.

The Role of Standardization and Interoperability

Standardization holds paramount importance when adopting and executing post-quantum cryptography within blockchain systems. Institutions like the National Institute of Standards and Technology have introduced competitions and evaluations to pinpoint and standardize post-quantum cryptographic algorithms. This standardization process confirms interoperability, cultivates trust, and facilitates widespread utilization of these algorithms across varied blockchain networks.

Test Implementations and Real-Life Evaluation

Multiple pilot projects and initiatives are launched to gauge the feasibility and practicability of p-q cryptography in actual blockchain settings. These implementations aid in pinpointing potential difficulties, performance consequences, and security considerations associated with merging post-quantum cryptography into existing blockchain infrastructures. The knowledge acquired from these pilot projects contributes to refining and enhancing post-quantum cryptographic algorithms for appropriateness within blockchain networks.

Evaluating Solutions for Post Quantum Cryptography Signature Verification

Hash-Based Signatures

Signature schemes based on hash functions, such as Lamport and Winternitz one-time signature schemes, provide post-quantum security due to the computational difficulty of hash functions. Although these schemes offer robust security assurances against quantum attacks, their large signature sizes make them less practical for bandwidth-restricted blockchain networks. Hash-based signatures are appropriate for situations where signature size is not a major concern, like in offline or low-bandwidth contexts.

Lattice-Based Signatures

BLISS and Dilithium schemes are examples of lattice-based signature schemes that leverage the difficulty of specific mathematical problems on lattices to ensure post-quantum security. These schemes have smaller signature sizes than hash-based signatures, rendering them more appropriate for resource-limited blockchain networks. Lattice-based signatures strike a good balance between security and efficiency; however, lattice operations' complexity can affect their performance.

Code-Based Signatures

Error-correcting codes are utilized in code-based signature schemes like McEliece and Niederreiter to provide quantum attack resistance. These schemes have small signature sizes and rapid signature generation capabilities, making them appealing for high-throughput blockchain systems. Nevertheless, code-based signatures may have larger public key sizes compared to other p-q cryptography signature schemes. This can influence storage requirements.

Multivariate-Based Signatures

Rainbow and HFE are multivariate-based signature schemes that rely on the difficulty of solving multivariate polynomial equation systems for post-quantum security. These schemes provide compact signature sizes and efficient signature verification, making them suitable for resource-limited blockchain networks. However, multivariate-based signatures can be prone to specific attacks, such as the Gröbner basis attack, necessitating cautious parameter selection and security analysis.

Hybrid Approaches

The integration of multiple post-quantum cryptography signature schemes characterizes hybrid approaches to capitalize on their respective benefits and address their shortcomings. A hybrid scheme can, for instance, merge a hash-based signature scheme for initial verification with a lattice-based or code-based signature scheme for additional validation. Hybrid approaches strive to deliver a sturdy and adaptable solution that harmonizes security, efficiency, and compatibility with existing cryptographic infrastructure.

When choosing a post-quantum cryptography signature verification solution for blockchain, it is critical to evaluate factors like security, signature size, computational efficiency, storage requirements, and protocol compatibility. The selection of a particular scheme will be determined by the blockchain network's specific demands and limitations.

It is important to note that it remains a developing field, with ongoing research and progress constantly enhancing signature schemes' efficiency and security. Keeping abreast of the latest developments and seeking advice from cryptographic experts is essential when making informed decisions regarding the adoption and implementation of it signature verification solutions in blockchain systems.

Blockchain developers and organizations can choose suitable post-quantum cryptography signature verification schemes by meticulously evaluating and comparing available options, ensuring robust defense against quantum attacks while maintaining optimal performance and scalability levels.

Moving Towards Standardization and Compatibility in Post-Quantum Cryptography:

The significance of standardization grows, enabling interoperability and compatibility among diverse blockchain networks. The adoption of post-quantum cryptographic algorithms and secure digital communication relies heavily on standardization. In this section, we will explore standardization's importance and the developments made thus far.

Standardization of Post-Quantum Cryptography by NIST

  • The National Institute of Standards and Technology (NIST) is at the forefront of standardizing post-quantum cryptography.
  • In 2017, NIST launched a public contest inviting submissions for post-quantum cryptography candidate algorithms across various categories, such as encryption, signature, and key exchange.
  • This contest seeks to pinpoint and select quantum-resistant algorithms that are efficient, robust, and can be widely implemented across various applications and sectors.
  • Currently in its final stages, the competition is narrowing down several algorithms for potential post-quantum cryptography standards.

Challenges in Interoperability and Compatibility:

  • Attaining compatibility and interoperability among different cryptographic algorithms and blockchain networks is a complicated feat.
  • Current blockchain systems often depend on specific cryptographic protocols and primitives that may not align with post-quantum algorithms.
  • A seamless shift demands thorough examination of backward compatibility, migration strategies, and consensus from participants.
  • Collaborative initiatives are essential for creating standards and protocols capable of smoothly integrating post-quantum cryptographic algorithms into existing blockchain networks.

Advantages of Standardization for Blockchain Networks:

  • The adoption of post-quantum cryptography by blockchain networks brings numerous benefits through standardization.
  • A common framework for cryptographic operations ensures interoperability, enabling secure communication among various blockchain platforms.
  • Algorithms undergoing standardization are rigorously assessed by the cryptography community, instilling confidence in their reliability and security.
  • Additionally, standardized frameworks simplify the integration of new cryptographic technologies and future enhancements.

Expanding Post-Quantum Cryptography to Additional Blockchain Networks:

The implementation of post-quantum cryptography spans beyond any single blockchain network or protocol. To guarantee long-term security and robustness of their systems, multiple blockchain platforms investigate ways to integrate post-quantum cryptographic algorithms as the quantum threat emerges. In this section, we will examine ongoing efforts to introduce post-quantum cryptography to other blockchain networks.

Ethereum and Post-Quantum Cryptography:

  • As one of the most prevalent blockchain platforms, Ethereum actively investigates the adoption of post-quantum cryptographic algorithms.
  • The Ethereum Foundation and its community engage in ongoing dialogue and partnerships with experts to evaluate the feasibility and appropriateness of various post-quantum algorithms for Ethereum's infrastructure.
  • Developing a roadmap for incorporating post-quantum cryptography that considers the potential impact on performance, scalability, and backward compatibility is the ultimate goal.

Other Blockchain Networks:

  • Outside of Ethereum, additional blockchain networks recognize the value of post-quantum cryptography.
  • Platforms like Hyperledger, Corda, and Polkadot proactively explore how quantum-resistant algorithms can be integrated into their protocols to counter emerging threats.
  • Collaborative work focuses on assessing and testing different post-quantum cryptographic solutions within real-world blockchain settings, taking into account factors such as performance, security, and infrastructure compatibility.

By expanding post-quantum cryptography to various blockchain networks, the goal is to construct a more secure and future-proof foundation for decentralized applications and digital asset transactions. Collaboration between standardization organizations, cryptographic experts, and blockchain platforms is vital in achieving

Conclusion

In conclusion, post-quantum cryptography offers a promising solution to address the quantum threat in blockchain. Efforts are underway to develop efficient and secure algorithms for post-quantum signature verification. Standardization and compatibility initiatives are crucial for seamless integration across different blockchain networks. The industry is actively working towards extending pq cryptography to ensure the security of blockchain transactions.

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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?
Table of Contents

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

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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!

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