Game of Trust: How Trustworthy Does Your Blockchain Think You Are?
Petros Drineas, Rohit Nema, Rafail Ostrovsky, Vassilis Zikas

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…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Cryptography and Data Security · Auction Theory and Applications
MethodsSparse Evolutionary Training
