V3rified: Revelation vs Non-Revelation Mechanisms for Decentralized Verifiable Computation

TL;DR

This paper explores the balance between decentralization and efficiency in verifiable computation within Web3, comparing revelation mechanisms like auctions with non-revelation approaches to understand their capabilities and limitations.

Contribution

It provides a complete characterization of the power and limitations of revelation versus non-revelation mechanisms in decentralized verifiable computation.

Findings

Revelation mechanisms can incentivize truthful bidding in decentralized settings.

Non-revelation mechanisms offer simplicity but may lack optimal incentives.

Trade-offs exist between decentralization, efficiency, and strategic behavior.

Abstract

In the era of Web3, decentralized technologies have emerged as the cornerstone of a new digital paradigm. Backed by a decentralized blockchain architecture, the Web3 space aims to democratize all aspects of the web. From data-sharing to learning models, outsourcing computation is an established, prevalent practice. Verifiable computation makes this practice trustworthy as clients/users can now efficiently validate the integrity of a computation. As verifiable computation gets considered for applications in the Web3 space, decentralization is crucial for system reliability, ensuring that no single entity can suppress clients. At the same time, however, decentralization needs to be balanced with efficiency: clients want their computations done as quickly as possible. Motivated by these issues, we study the trade-off between decentralization and efficiency when outsourcing computational tasks to strategic, rational solution providers. Specifically, we examine this trade-off when the client employs (1) revelation mechanisms, i.e. auctions, where solution providers bid their desired reward for completing the task by a specific deadline and then the client selects which of them will do the task and how much they will be rewarded, and (2) simple, non-revelation mechanisms, where the client commits to the set of rules she will use to map solutions at specific times to rewards and then solution providers decide whether they want to do the task or not. We completely characterize the power and limitations of revelation and non-revelation mechanisms in our model.