Zero-Knowledge Proofs: A Technology Primer TEST

BY Max McGarrigle, Carlos Monteiro

Maximal extractable value (“MEV”) is defined as the value that block proposers (miners or validators) can extract from a blockchain by using their ability to order, insert and censor transactions within the blocks that they produce.

To understand MEV extraction, we need to understand the lifecycle of a blockchain transaction (figure 1). It starts with the user having the intent to use a blockchain application. The wallet is an application that encodes this intention into a transaction that a blockchain can understand. This transaction is then forwarded to a node on the blockchain but does not get immediately included in the chain or get executed.

1. Introduction
2. Why do we care?
3. Zero-knowledge Proofs

Part One

Introduction

In recent months, zero-knowledge proofs have seen both significant academic development and impactful practical implementations across crypto. From zero-knowledge rollups to private chains, major advances are being made towards developing novel products and increasing usability for existing ones. The speed of development in this area can quickly obscure the progress that has been made. The purpose of this report is to set out the current state of research, discuss the most recent developments, and explore the practical implementations of said research.

Part Two

Why do we care?

Zero-knowledge proofs are primarily used for two things: Ensuring computations are correct, and enforcing privacy.

1. Verifiable computation is a method of proving that we have correctly performed a calculation. We can use such technology to design scalability solutions, such as zero-knowledge rollups, as well as other novel structures like shared execution layers. The zero-knowledge rollup sector has seen a significant leap in traction driven by their provision of a solution to the particularly high fees and low throughput on certain L1s such as Ethereum. Verifiable computation has its applications off-chain as well – for example, proving that a critical program (e.g. one that outputs medical decisions or directs large financial transactions) has executed correctly, so that we are
sure no errors have occurred. The key here is that we can verify in milliseconds the execution of a program that might have originally taken hours to run, rather than having to check it by re-running.
2. Privacy is another major feature, and one that may prove vital in inducing widespread adoption. Zero-knowledge proofs can be used to attest to the validity of private transactions, allowing us to maintain trustlessness and consensus without disclosing the contents of any transaction. Naturally, this does not only have to happen on the base layer – this could be implemented in an off-chain context, generating/verifying the proofs on individual devices, which might be more suitable in cases where we care more about costs and less about decentralization.

In this report, we focus on the first of these, exploring the state of research surrounding zero-knowledge proofs, their implications, and the potential focus of future works.

Part Three

Zero-knowledge Proofs

Zero-knowledge proofs (“ZKPs”) are a cryptographic method of proving whether or not a statement is true without giving away any other information.

For example, consider the following, in which we want to show someone that we know where Wally is, yet do not want to reveal the location itself [1].

Figure 1: Venn Diagram of the different MEV categories.

Table 1.1: Timeline of ZKP Systems (Source: Wikipedia)

Footnotes

1.
A popular explanation for ZKPs used in many sources. While we are unsure of its origin, the earliest mention we found was in a 2008 lecture by Mike Rosulek at the University of Montana. https://web.engr.oregonstate.edu/~rosulekm/pubs/zk-waldo-talk.pdf
The source for the figures is a presentation by Beanstalk Network: https://docs.google.com/presentation/d/1gfB6WZMvM9mmDKofFibIgsyYShdf0RV_Y8TLz3k1Ls0/edit#slide=id.p

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