MeritRank: Sybil Tolerant Reputation for Merit-based Tokenomics
Nasrulin, B. (TU Delft Distributed Systems)
Ishmaev, G. (TU Delft Distributed Systems)
Pouwelse, J.A. (TU Delft Distributed Systems)
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.
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Proceedings of the 2022 4th Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS)
2022 4th Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS), 2022-09-27 → 2022-09-30, Paris, France
2022 4th Conference on Blockchain Research and Applications for Innovative Networks and Services, BRAINS 2022
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© 2022 B. Nasrulin, G. Ishmaev, J.A. Pouwelse