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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Aethir Tokenomics – Case Study

Kajetan Olas

22 Nov 2024
Aethir Tokenomics – Case Study

Authors of the contents are not affiliated to the reviewed project in any way and none of the information presented should be taken as financial advice.

In this article we analyze tokenomics of Aethir - a project providing on-demand cloud compute resources for the AI, Gaming, and virtualized compute sectors.
Aethir aims to aggregate enterprise-grade GPUs from multiple providers into a DePIN (Decentralized Physical Infrastructure Network). Its competitive edge comes from utlizing the GPUs for very specific use-cases, such as low-latency rendering for online games.
Due to decentralized nature of its infrastructure Aethir can meet the demands of online-gaming in any region. This is especially important for some gamer-abundant regions in Asia with underdeveloped cloud infrastructure that causes high latency ("lags").
We will analyze Aethir's tokenomics, give our opinion on what was done well, and provide specific recommendations on how to improve it.

Evaluation Summary

Aethir Tokenomics Structure

The total supply of ATH tokens is capped at 42 billion ATH. This fixed cap provides a predictable supply environment, and the complete emissions schedule is listed here. As of November 2024 there are approximately 5.2 Billion ATH in circulation. In a year from now (November 2025), the circulating supply will almost triple, and will amount to approximately 15 Billion ATH. By November 2028, today's circulating supply will be diluted by around 86%.

From an investor standpoint the rational decision would be to stake their tokens and hope for rewards that will balance the inflation. Currently the estimated APR for 3-year staking is 195% and for 4-year staking APR is 261%. The rewards are paid out weekly. Furthermore, stakers can expect to get additional rewards from partnered AI projects.

Staking Incentives

Rewards are calculated based on the staking duration and staked amount. These factors are equally important and they linearly influence weekly rewards. This means that someone who stakes 100 ATH for 2 weeks will have the same weekly rewards as someone who stakes 200 ATH for 1 week. This mechanism greatly emphasizes long-term holding. That's because holding a token makes sense only if you go for long-term staking. E.g. a whale staking $200k with 1 week lockup. will have the same weekly rewards as person staking $1k with 4 year lockup. Furthermore the ATH staking rewards are fixed and divided among stakers. Therefore Increase of user base is likely to come with decrease in rewards.
We believe the main weak-point of Aethirs staking is the lack of equivalency between rewards paid out to the users and value generated for the protocol as a result of staking.

Token Distribution

The token distribution of $ATH is well designed and comes with long vesting time-frames. 18-month cliff and 36-moths subsequent linear vesting is applied to team's allocation. This is higher than industry standard and is a sign of long-term commitment.

  • Checkers and Compute Providers: 50%
  • Ecosystem: 15%
  • Team: 12.5%
  • Investors: 11.5%
  • Airdrop: 6%
  • Advisors: 5%

Aethir's airdrop is divided into 3 phases to ensure that only loyal users get rewarded. This mechanism is very-well thought and we rate it highly. It fosters high community engagement within the first months of the project and sets the ground for potentially giving more-control to the DAO.

Governance and Community-Led Development

Aethir’s governance model promotes community-led decision-making in a very practical way. Instead of rushing with creation of a DAO for PR and marketing purposes Aethir is trying to make it the right way. They support projects building on their infrastructure and regularly share updates with their community in the most professional manner.

We believe Aethir would benefit from implementing reputation boosted voting. An example of such system is described here. The core assumption is to abandon the simplistic: 1 token = 1 vote and go towards: Votes = tokens * reputation_based_multiplication_factor.

In the attached example, reputation_based_multiplication_factor rises exponentially with the number of standard deviations above norm, with regard to user's rating. For compute compute providers at Aethir, user's rating could be replaced by provider's uptime.

Perspectives for the future

While it's important to analyze aspects such as supply-side tokenomics, or governance, we must keep in mind that 95% of project's success depends on demand-side. In this regard the outlook for Aethir may be very bright. The project declares $36M annual reccuring revenue. Revenue like this is very rare in the web3 space. Many projects are not able to generate any revenue after succesfull ICO event, due to lack fo product-market-fit.

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

Quadratic Voting in Web3

Kajetan Olas

04 Dec 2024
Quadratic Voting in Web3

Decentralized systems are reshaping how we interact, conduct transactions, and govern online communities. As Web3 continues to advance, the necessity for effective and fair voting mechanisms becomes apparent. Traditional voting systems, such as the one-token-one-vote model, often fall short in capturing the intensity of individual preferences, which can result in centralization. Quadratic Voting (QV) addresses this challenge by enabling individuals to express not only their choices but also the strength of their preferences.

In QV, voters are allocated a budget of credits that they can spend to cast votes on various issues. The cost of casting multiple votes on a single issue increases quadratically, meaning that each additional vote costs more than the last. This system allows for a more precise expression of preferences, as individuals can invest more heavily in issues they care deeply about while conserving credits on matters of lesser importance.

Understanding Quadratic Voting

Quadratic Voting (QV) is a voting system designed to capture not only the choices of individuals but also the strength of their preferences. In most DAO voting mechanisms, each person typically has one vote per token, which limits the ability to express how strongly they feel about a particular matter. Furthermore, QV limits the power of whales and founding team who typically have large token allocations. These problems are adressed by making the cost of each additional vote increase quadratically.

In QV, each voter is given a budget of credits or tokens that they can spend to cast votes on various issues. The key principle is that the cost to cast n votes on a single issue is proportional to the square of n. This quadratic cost function ensures that while voters can express stronger preferences, doing so requires a disproportionately higher expenditure of their voting credits. This mechanism discourages voters from concentrating all their influence on a single issue unless they feel very strongly about it. In the context of DAOs, it means that large holders will have a hard-time pushing through with a proposal if they'll try to do it on their own.

Practical Example

Consider a voter who has been allocated 25 voting credits to spend on several proposals. The voter has varying degrees of interest in three proposals: Proposal A, Proposal B, and Proposal C.

  • Proposal A: High interest.
  • Proposal B: Moderate interest.
  • Proposal C: Low interest.

The voter might allocate their credits as follows:

Proposal A:

  • Votes cast: 3
  • Cost: 9 delegated tokens

Proposal B:

  • Votes cast: 2
  • Cost: 4 delegated tokens

Proposal C:

  • Votes cast: 1
  • Cost: 1 delegated token

Total delegated tokens: 14
Remaining tokens: 11

With the remaining tokens, the voter can choose to allocate additional votes to the proposals based on their preferences or save for future proposals. If they feel particularly strong about Proposal A, they might decide to cast one more vote:

Additional vote on Proposal A:

  • New total votes: 4
  • New cost: 16 delegated tokens
  • Additional cost: 16−9 = 7 delegated tokens

Updated total delegated tokens: 14+7 = 21

Updated remaining tokens: 25−21 = 425 - 21 = 4

This additional vote on Proposal A costs 7 credits, significantly more than the previous vote, illustrating how the quadratic cost discourages excessive influence on a single issue without strong conviction.

Benefits of Implementing Quadratic Voting

Key Characteristics of the Quadratic Cost Function

  • Marginal Cost Increases Linearly: The marginal cost of each additional vote increases linearly. The cost difference between casting n and n−1 votes is 2n−1.
  • Total Cost Increases Quadratically: The total cost to cast multiple votes rises steeply, discouraging voters from concentrating too many votes on a single issue without significant reason.
  • Promotes Egalitarian Voting: Small voters are encouraged to participate, because relatively they have a much higher impact.

Advantages Over Traditional Voting Systems

Quadratic Voting offers several benefits compared to traditional one-person-one-vote systems:

  • Captures Preference Intensity: By allowing voters to express how strongly they feel about an issue, QV leads to outcomes that better reflect the collective welfare.
  • Reduces Majority Domination: The quadratic cost makes it costly for majority groups to overpower minority interests on every issue.
  • Encourages Honest Voting: Voters are incentivized to allocate votes in proportion to their true preferences, reducing manipulation.

By understanding the foundation of Quadratic Voting, stakeholders in Web3 communities can appreciate how this system supports more representative governance.

Conclusion

Quadratic voting is a novel voting system that may be used within DAOs to foster decentralization. The key idea is to make the cost of voting on a certain issue increase quadratically. The leading player that makes use of this mechanism is Optimism. If you're pondering about the design of your DAO, we highly recommend taking a look at their research on quadratic funding.

If you're looking to create a robust governance model and go through institutional-grade testing please reach out to contact@nextrope.com. Our team is ready to help you with the token engineering process and ensure that your DAO will stand out as a beacon of innovation and resilience in the long term.