Adopting Open Multiple ZK-EVM: Challenges and Solutions

According to reports, in his latest blog article, V Shen suggested adopting open multiple ZK-EVM to create a “multi client” ecosystem, but at the same time, he pointed out that this solution would face two major challenges: latency and data inefficiency. A malicious attacker may delay the release of a block, as well as effective proof for a client. If the time is long enough, it may create a temporary fork and break the chain of several slots. In addition, if you want to be able to generate multiple types of proofs for a block, you need to actually publish the original signature, resulting in data inefficiency.

V God: Using multiple open ZK-EVM will face two major challenges: latency and data inefficiency

Introduction

In the world of blockchain technology, the development of solutions that can enhance its capability and efficiency is always ongoing. One of the latest solutions that has been proposed is adopting open multiple ZK-EVM to create a “multi client” ecosystem. In his latest blog article, V Shen suggested that this solution has the potential to significantly improve the performance of blockchain systems. However, he also pointed out that this solution would face two major challenges: latency and data inefficiency. In this article, we will explore these challenges, and discuss potential solutions that can help overcome them.

Understanding the Challenges

Latency

When adopting open multiple ZK-EVM, one of the primary challenges that arise is latency. As a malicious attacker can delay the release of a block or effective proof for a client, it may create a temporary fork and break the chain of several slots. This can be a major problem as it can cause disruptions in the system and affect its stability. While there are various ways to mitigate this risk, it is still a challenge that needs to be addressed.

Data Inefficiency

Another challenge that arises when adopting open multiple ZK-EVM is data inefficiency. If you want to be able to generate multiple types of proofs for a block, you need to actually publish the original signature. This results in data inefficiency that can slow down the system and create bottlenecks. Solving this issue is essential for making the proposed solution viable and effective.

Solutions to the Challenges

Latency

To address the issue of latency, several solutions have been proposed. One of the most promising is the use of adaptive zk-SNARK verification. This solution can significantly reduce verification time and ensure that the system remains stable even in the face of attacks. Another solution is the use of block finality monitoring to detect and prevent temporary forks. These solutions need to be explored and implemented to ensure that the proposed solution can be used effectively.

Data Inefficiency

To overcome the challenge of data inefficiency, there are several solutions that can be explored. One of the most effective is the use of Bloom filters to reduce the size of the proof data. This can help improve the efficiency of the system and reduce the amount of data that needs to be processed. Additionally, the use of Merkle trees can help ensure that the data is stored efficiently and securely.

Conclusion

Adopting open multiple ZK-EVM seems to be a promising solution for creating a “multi client” ecosystem in blockchain technology. However, it is essential to overcome the challenges of latency and data inefficiency to make it a viable option. Through the use of adaptive zk-SNARK verification, block finality monitoring, Bloom filters, and Merkle trees, these challenges can be overcome. It is essential to explore and implement these solutions to ensure that the proposed solution can be used to enhance the performance of blockchain systems.

FAQs

Q: What is open multiple ZK-EVM?

A: Open multiple ZK-EVM is a solution that proposes to create a “multi client” ecosystem in blockchain technology.

Q: What are the challenges of adopting open multiple ZK-EVM?

A: The major challenges of adopting open multiple ZK-EVM are latency and data inefficiency.

Q: What are the potential solutions to overcome these challenges?

A: Adaptive zk-SNARK verification, block finality monitoring, Bloom filters, and Merkle trees are potential solutions that can help overcome the challenges of adopting open multiple ZK-EVM.