How to Implement Zero-Knowledge Proof in Blockchain Applications


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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Applying Game Theory in Token Design

Kajetan Olas

16 Apr 2024
Applying Game Theory in Token Design

Blockchain technology allows for aligning incentives among network participants by rewarding desired behaviors with tokens.
But there is more to it than simply fostering cooperation. Game theory allows for designing incentive-machines that can't be turned-off and resemble artificial life.

Emergent Optimization

Game theory provides a robust framework for analyzing strategic interactions with mathematical models, which is particularly useful in blockchain environments where multiple stakeholders interact within a set of predefined rules. By applying this framework to token systems, developers can design systems that influence the emergent behaviors of network participants. This ensures the stability and effectiveness of the ecosystem.

Bonding Curves

Bonding curves are tool used in token design to manage the relationship between price and token supply predictably. Essentially, a bonding curve is a mathematical curve that defines the price of a token based on its supply. The more tokens that are bought, the higher the price climbs, and vice versa. This model incentivizes early adoption and can help stabilize a token’s economy over time.

For example, a bonding curve could be designed to slow down price increases after certain milestones are reached, thus preventing speculative bubbles and encouraging steadier, more organic growth.

The Case of Bitcoin

Bitcoin’s design incorporates game theory, most notably through its consensus mechanism of proof-of-work (PoW). Its reward function optimizes for security (hashrate) by optimizing for maximum electricity usage. Therefore, optimizing for its legitimate goal of being secure also inadvertently optimizes for corrupting natural environment. Another emergent outcome of PoW is the creation of mining pools, that increase centralization.

The Paperclip Maximizer and the dangers of blockchain economy

What’s the connection between AI from the story and decentralized economies? Blockchain-based incentive systems also can’t be turned off. This means that if we design an incentive system that optimizes towards a wrong objective, we might be unable to change it. Bitcoin critics argue that the PoW consensus mechanism optimizes toward destroying planet Earth.

Layer 2 Solutions

Layer 2 solutions are built on the understanding that the security provided by this core kernel of certainty can be used as an anchor. This anchor then supports additional economic mechanisms that operate off the blockchain, extending the utility of public blockchains like Ethereum. These mechanisms include state channels, sidechains, or plasma, each offering a way to conduct transactions off-chain while still being able to refer back to the anchored security of the main chain if necessary.

Conceptual Example of State Channels

State channels allow participants to perform numerous transactions off-chain, with the blockchain serving as a backstop in case of disputes or malfeasance.

Consider two players, Alice and Bob, who want to play a game of tic-tac-toe with stakes in Ethereum. The naive approach would be to interact directly with a smart contract for every move, which would be slow and costly. Instead, they can use a state channel for their game.

  1. Opening the Channel: They start by deploying a "Judge" smart contract on Ethereum, which holds the 1 ETH wager. The contract knows the rules of the game and the identities of the players.
  2. Playing the Game: Alice and Bob play the game off-chain by signing each move as transactions, which are exchanged directly between them but not broadcast to the blockchain. Each transaction includes a nonce to ensure moves are kept in order.
  3. Closing the Channel: When the game ends, the final state (i.e., the sequence of moves) is sent to the Judge contract, which pays out the wager to the winner after confirming both parties agree on the outcome.

A threat stronger than the execution

If Bob tries to cheat by submitting an old state where he was winning, Alice can challenge this during a dispute period by submitting a newer signed state. The Judge contract can verify the authenticity and order of these states due to the nonces, ensuring the integrity of the game. Thus, the mere threat of execution (submitting the state to the blockchain and having the fraud exposed) secures the off-chain interactions.

Game Theory in Practice

Understanding the application of game theory within blockchain and token ecosystems requires a structured approach to analyzing how stakeholders interact, defining possible actions they can take, and understanding the causal relationships within the system. This structured analysis helps in creating effective strategies that ensure the system operates as intended.

Stakeholder Analysis

Identifying Stakeholders

The first step in applying game theory effectively is identifying all relevant stakeholders within the ecosystem. This includes direct participants such as users, miners, and developers but also external entities like regulators, potential attackers, and partner organizations. Understanding who the stakeholders are and what their interests and capabilities are is crucial for predicting how they might interact within the system.

Stakeholders in blockchain development for systems engineering

Assessing Incentives and Capabilities

Each stakeholder has different motivations and resources at their disposal. For instance, miners are motivated by block rewards and transaction fees, while users seek fast, secure, and cheap transactions. Clearly defining these incentives helps in predicting how changes to the system’s rules and parameters might influence their behaviors.

Defining Action Space

Possible Actions

The action space encompasses all possible decisions or strategies stakeholders can employ in response to the ecosystem's dynamics. For example, a miner might choose to increase computational power, a user might decide to hold or sell tokens, and a developer might propose changes to the protocol.

Artonomus, Github

Constraints and Opportunities

Understanding the constraints (such as economic costs, technological limitations, and regulatory frameworks) and opportunities (such as new technological advancements or changes in market demand) within which these actions take place is vital. This helps in modeling potential strategies stakeholders might adopt.

Artonomus, Github

Causal Relationships Diagram

Mapping Interactions

Creating a diagram that represents the causal relationships between different actions and outcomes within the ecosystem can illuminate how complex interactions unfold. This diagram helps in identifying which variables influence others and how they do so, making it easier to predict the outcomes of certain actions.

Artonomus, Github

Analyzing Impact

By examining the causal relationships, developers and system designers can identify critical leverage points where small changes could have significant impacts. This analysis is crucial for enhancing system stability and ensuring its efficiency.

Feedback Loops

Understanding feedback loops within a blockchain ecosystem is critical as they can significantly amplify or mitigate the effects of changes within the system. These loops can reinforce or counteract trends, leading to rapid growth or decline.

Reinforcing Loops

Reinforcing loops are feedback mechanisms that amplify the effects of a trend or action. For example, increased adoption of a blockchain platform can lead to more developers creating applications on it, which in turn leads to further adoption. This positive feedback loop can drive rapid growth and success.

Death Spiral

Conversely, a death spiral is a type of reinforcing loop that leads to negative outcomes. An example might be the increasing cost of transaction fees leading to decreased usage of the blockchain, which reduces the incentive for miners to secure the network, further decreasing system performance and user adoption. Identifying potential death spirals early is crucial for maintaining the ecosystem's health.

The Death Spiral: How Terra's Algorithmic Stablecoin Came Crashing Down
the-death-spiral-how-terras-algorithmic-stablecoin-came-crashing-down/, Forbes


The fundamental advantage of token-based systems is being able to reward desired behavior. To capitalize on that possibility, token engineers put careful attention into optimization and designing incentives for long-term growth.


  1. What does game theory contribute to blockchain token design?
    • Game theory optimizes blockchain ecosystems by structuring incentives that reward desired behavior.
  2. How do bonding curves apply game theory to improve token economics?
    • Bonding curves set token pricing that adjusts with supply changes, strategically incentivizing early purchases and penalizing speculation.
  3. What benefits do Layer 2 solutions provide in the context of game theory?
    • Layer 2 solutions leverage game theory, by creating systems where the threat of reporting fraudulent behavior ensures honest participation.

Token Engineering Process

Kajetan Olas

13 Apr 2024
Token Engineering Process

Token Engineering is an emerging field that addresses the systematic design and engineering of blockchain-based tokens. It applies rigorous mathematical methods from the Complex Systems Engineering discipline to tokenomics design.

In this article, we will walk through the Token Engineering Process and break it down into three key stages. Discovery Phase, Design Phase, and Deployment Phase.

Discovery Phase of Token Engineering Process

The first stage of the token engineering process is the Discovery Phase. It focuses on constructing high-level business plans, defining objectives, and identifying problems to be solved. That phase is also the time when token engineers first define key stakeholders in the project.

Defining the Problem

This may seem counterintuitive. Why would we start with the problem when designing tokenomics? Shouldn’t we start with more down-to-earth matters like token supply? The answer is No. Tokens are a medium for creating and exchanging value within a project’s ecosystem. Since crypto projects draw their value from solving problems that can’t be solved through TradFi mechanisms, their tokenomics should reflect that. 

The industry standard, developed by McKinsey & Co. and adapted to token engineering purposes by Outlier Ventures, is structuring the problem through a logic tree, following MECE.
MECE stands for Mutually Exclusive, Collectively Exhaustive. Mutually Exclusive means that problems in the tree should not overlap. Collectively Exhaustive means that the tree should cover all issues.

In practice, the “Problem” should be replaced by a whole problem statement worksheet. The same will hold for some of the boxes.
A commonly used tool for designing these kinds of diagrams is the Miro whiteboard.

Identifying Stakeholders and Value Flows in Token Engineering

This part is about identifying all relevant actors in the ecosystem and how value flows between them. To illustrate what we mean let’s consider an example of NFT marketplace. In its case, relevant actors might be sellers, buyers, NFT creators, and a marketplace owner. Possible value flow when conducting a transaction might be: buyer gets rid of his tokens, seller gets some of them, marketplace owner gets some of them as fees, and NFT creators get some of them as royalties.

Incentive Mechanisms Canvas

The last part of what we consider to be in the Discovery Phase is filling the Incentive Mechanisms Canvas. After successfully identifying value flows in the previous stage, token engineers search for frictions to desired behaviors and point out the undesired behaviors. For example, friction to activity on an NFT marketplace might be respecting royalty fees by marketplace owners since it reduces value flowing to the seller.


Design Phase of Token Engineering Process

The second stage of the Token Engineering Process is the Design Phase in which you make use of high-level descriptions from the previous step to come up with a specific design of the project. This will include everything that can be usually found in crypto whitepapers (e.g. governance mechanisms, incentive mechanisms, token supply, etc). After finishing the design, token engineers should represent the whole value flow and transactional logic on detailed visual diagrams. These diagrams will be a basis for creating mathematical models in the Deployment Phase. 

Token Engineering Artonomous Design Diagram
Artonomous design diagram, source: Artonomous GitHub

Objective Function

Every crypto project has some objective. The objective can consist of many goals, such as decentralization or token price. The objective function is a mathematical function assigning weights to different factors that influence the main objective in the order of their importance. This function will be a reference for machine learning algorithms in the next steps. They will try to find quantitative parameters (e.g. network fees) that maximize the output of this function.
Modified Metcalfe’s Law can serve as an inspiration during that step. It’s a framework for valuing crypto projects, but we believe that after adjustments it can also be used in this context.

Deployment Phase of Token Engineering Process

The Deployment Phase is final, but also the most demanding step in the process. It involves the implementation of machine learning algorithms that test our assumptions and optimize quantitative parameters. Token Engineering draws from Nassim Taleb’s concept of Antifragility and extensively uses feedback loops to make a system that gains from arising shocks.

Agent-based Modelling 

In agent-based modeling, we describe a set of behaviors and goals displayed by each agent participating in the system (this is why previous steps focused so much on describing stakeholders). Each agent is controlled by an autonomous AI and continuously optimizes his strategy. He learns from his experience and can mimic the behavior of other agents if he finds it effective (Reinforced Learning). This approach allows for mimicking real users, who adapt their strategies with time. An example adaptive agent would be a cryptocurrency trader, who changes his trading strategy in response to experiencing a loss of money.

Monte Carlo Simulations

Token Engineers use the Monte Carlo method to simulate the consequences of various possible interactions while taking into account the probability of their occurrence. By running a large number of simulations it’s possible to stress-test the project in multiple scenarios and identify emergent risks.

Testnet Deployment

If possible, it's highly beneficial for projects to extend the testing phase even further by letting real users use the network. Idea is the same as in agent-based testing - continuous optimization based on provided metrics. Furthermore, in case the project considers airdropping its tokens, giving them to early users is a great strategy. Even though part of the activity will be disingenuine and airdrop-oriented, such strategy still works better than most.

Time Duration

Token engineering process may take from as little as 2 weeks to as much as 5 months. It depends on the project category (Layer 1 protocol will require more time, than a simple DApp), and security requirements. For example, a bank issuing its digital token will have a very low risk tolerance.

Required Skills for Token Engineering

Token engineering is a multidisciplinary field and requires a great amount of specialized knowledge. Key knowledge areas are:

  • Systems Engineering
  • Machine Learning
  • Market Research
  • Capital Markets
  • Current trends in Web3
  • Blockchain Engineering
  • Statistics


The token engineering process consists of 3 steps: Discovery Phase, Design Phase, and Deployment Phase. It’s utilized mostly by established blockchain projects, and financial institutions like the International Monetary Fund. Even though it’s a very resource-consuming process, we believe it’s worth it. Projects that went through scrupulous design and testing before launch are much more likely to receive VC funding and be in the 10% of crypto projects that survive the bear market. Going through that process also has a symbolic meaning - it shows that the project is long-term oriented.

If you're looking to create a robust tokenomics model and go through institutional-grade testing please reach out to Our team is ready to help you with the token engineering process and ensure your project’s resilience in the long term.


What does token engineering process look like?

  • Token engineering process is conducted in a 3-step methodical fashion. This includes Discovery Phase, Design Phase, and Deployment Phase. Each of these stages should be tailored to the specific needs of a project.

Is token engineering meant only for big projects?

  • We recommend that even small projects go through a simplified design and optimization process. This increases community's trust and makes sure that the tokenomics doesn't have any obvious flaws.

How long does the token engineering process take?

  • It depends on the project and may range from 2 weeks to 5 months.