Game of Trust: How Trustworthy Does Your Blockchain Think You Are?

TL;DR

This paper proposes a novel reputation system for blockchains that uses a PageRank-inspired algorithm and game theory to assess and incentivize trustworthiness among nodes, improving blockchain robustness and scalability.

Contribution

It introduces a new framework combining PageRank adaptation and Trustworthy Reputation games to create incentivized reputation systems for blockchains.

Findings

Developed a PageRank-based trust information extraction method

Designed Trustworthy Reputation games for truthful reporting

Enabled robust reputation systems for blockchain applications

Abstract

We investigate how a blockchain can distill the collective belief of its nodes regarding the trustworthiness of a (sub)set of nodes into a {\em reputation system} that reflects the probability of correctly performing a task. To address this question, we introduce a framework that breaks it down into two sub-problems: 1. (Information Extraction): How can the system distill trust information from a function of the nodes' true beliefs? 2. (Incentive Design): How can we incentivize nodes to truthfully report such information? To tackle the first sub-problem, we adapt, in a non-trivial manner, the well-known PageRank algorithm to our problem. For the second, we define a new class of games, called Trustworthy Reputation games (TRep games), which aim to extract the collective beliefs on trust from the actions of rational participants. We then propose a concrete TRep game whose utility function leverages Personalized PageRank and can be instantiated through a straightforward blockchain rewards mechanism. Building on this, we show how the TRep game enables the design of a reputation system. Such systems can enhance the robustness, scalability, and efficiency of blockchain and DeFi solutions. For instance, we demonstrate how such a system can be used within a Proof-of-Reputation blockchain.