Altruism, reciprocity, and tokens to reward forwarding data: Is that fair?

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Publication Year
2024
Language
English
Research Group
Data-Intensive Systems
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Pages (from-to)
406-415
ISBN (print)
979-8-3503-1675-9
ISBN (electronic)
979-8-3503-1674-2
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Abstract

Decentralized storage networks offer services with intriguing possibilities to reduce inequalities in an extremely centralized market. Fair distribution of rewards, however, is still a persistent problem in the current generation of decentralized applications using token-based incentives. They are often disproportionally concentrated with small number of early adopters and high-resourced participants. Incentive mechanisms capable of addressing this problem are still poorly understood. This paper aims to help fill this gap by developing our Tit-forToken (Tit4Tok) model. Tit4Tok realizes incentives based on the triad of altruism (selfless behavior), reciprocity (Tit-for-Tat), and monetary rewards compatible with a free market. Tit4Tok analyzes the effects of storage-, and network-parameters fine-tuning to achieve fair distribution of rewards for participants. We present a comprehensive exploration of different factors when incentivized peers share bandwidth in a libp2p-based network, including uneven distributions emerging when gateways provide data to users outside the network. We quantified the Income-Fairness with the Gini coefficient, using multiple model instantiations and diverse approaches for debt cancellation. We propose regular changes to the gateway neighborhood and show that our shuffling method improves the Income-Fairness from 0.66 to 0.16. We quantified the non-negligible cost of tolerating free-riding (altruism). The performance is evaluated by extensive computer simulations and using an IPFS workload to study the effects of caching.

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