MeritRank

Sybil Tolerant Reputation for Merit-based Tokenomics

Conference paper (2022)

Authors

B. Nasrulin Data-Intensive Systems - EEMCS
G. Ishmaev Data-Intensive Systems - EEMCS
J.A. Pouwelse Data-Intensive Systems - EEMCS
Research Group
Data-Intensive Systems (EEMCS) (TU Delft)
Copyright: © 2022 B. Nasrulin, G. Ishmaev, J.A. Pouwelse
DOI: https://doi.org/10.1109/BRAINS55737.2022.9908685
Reputation Sybil attack Tokenomics Feedback Aggregation
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Published Date

2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Data-Intensive Systems

Abstract

Decentralized reputation schemes present a promising area of experimentation in blockchain applications. These solutions aim to overcome the shortcomings of simple monetary incentive mechanisms of naive tokenomics. However, there is a significant research gap regarding the limitations and benefits of such solutions. We formulate these trade-offs as a conjecture on the irreconcilability of three desirable properties of the reputation system in this context. Such a system can not be simultaneously generalizable, trustless, and Sybil resistant. To handle the limitations of this trilemma, we propose MeritRank: Sybil tolerant feedback aggregation mechanism for reputation. Instead of preventing Sybil attacks, our approach successfully bounds the benefits of these attacks. Using a dataset of participants’ interactions in MakerDAO, we run experiments to demonstrate Sybil tolerance of MeritRank. Decay parameters of reputation in MeritRank: transitivity decay and connectivity decay, allow for a fine-tuning of desirable levels of reputation utility and Sybil tolerance in different use contexts.