MeritRank: Sybil Tolerant Reputation for Merit-based Tokenomics

TL;DR

MeritRank introduces a decentralized, trustless reputation system that is resilient to Sybil attacks by employing decay mechanisms, enhancing tokenomics by reliably assessing diverse contributions without central oversight.

Contribution

This work presents MeritRank, a novel reputation system that effectively limits Sybil attack benefits through decay mechanisms, ensuring trustless and generalizable reputation assessment.

Findings

MeritRank demonstrates strong Sybil resistance in MakerDAO data.

Decay mechanisms effectively reduce the impact of fake identities.

The system supports diverse contribution evaluation across contexts.

Abstract

Decentralized reputation systems are emerging as promising mechanisms to enhance the effectiveness of token-based economies. Unlike traditional monetary incentives, these systems reward participants based on the actual value of their contributions to the network. However, the advantages and challenges associated with such systems remain largely unexplored. In this work, we investigate the inherent trade-offs in designing a decentralized reputation system that is simultaneously generalizable, trustless, and Sybil-resistant. Specifically, `generalizable' means that the system can assess various types of contributions across different contexts, `trustless' indicates that it functions without the need for a central authority to oversee reputations, and `Sybil-resistant' refers to its ability to withstand manipulations by fake identities, i.e., Sybil attacks. We propose MeritRank, a Sybil-tolerant reputation system based on feedback aggregation from participants. Instead of entirely preventing Sybil attacks, our approach effectively limits the benefits that attackers can gain from such strategies. This is achieved by reducing the perceived value of the attacker's and Sybil nodes' contributions through the application of decay mechanisms -- specifically, transitivity decay, connectivity decay, and epoch decay. Using a dataset of participant interactions in MakerDAO, we conducted experiments to demonstrate the Sybil tolerance of MeritRank.