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dc.contributor.authorLiao, Maizi
dc.date.accessioned2022-06-21 15:37:18 (GMT)
dc.date.available2023-06-22 04:50:05 (GMT)
dc.date.issued2022-06-21
dc.date.submitted2022-06-16
dc.identifier.urihttp://hdl.handle.net/10012/18415
dc.description.abstractFounded in 2017, Algorand is the first carbon-negative blockchain protocol inspired by proof of stake. Algorand uses a Byzantine agreement protocol to add new blocks to the blockchain. The protocol can tolerate malicious users as long as a supermajority of the stake is controlled by non-malicious users. The protocol achieves about 100x more throughput that Bitcoin and can be easily scaled to millions of nodes. Despite its impressive features, Algorand lacks a reward-distribution scheme to incentivize nodes to participate in the protocol. In this work, we study the incentive issue in Algorand through the lens of game theory. We model the Algorand protocol as a Bayesian game and propose a novel reward scheme to address the incentive issue in Algorand. Through rigorous analysis, we derive necessary conditions to ensure that participation in the protocol is a Bayesian Nash equilibrium even in the presence of a malicious adversary. In addition, we propose a referral mechanism to ensure that malicious nodes cannot earn more rewards in expectation compared to non-malicious nodes.en
dc.language.isoenen
dc.publisherUniversity of Waterlooen
dc.subjectblockchainen
dc.subjectgame theoryen
dc.subjectmechanism designen
dc.subjectreward schemeen
dc.subjectalgoranden
dc.titleDesigning an Incentive-compatible Reward Scheme for Algoranden
dc.typeMaster Thesisen
dc.pendingfalse
uws-etd.degree.departmentElectrical and Computer Engineeringen
uws-etd.degree.disciplineElectrical and Computer Engineeringen
uws-etd.degree.grantorUniversity of Waterlooen
uws-etd.degreeMaster of Applied Scienceen
uws-etd.embargo.terms1 yearen
uws.contributor.advisorZahedi, Seyed Majid
uws.contributor.advisorGolab, Wojciech
uws.contributor.affiliation1Faculty of Engineeringen
uws.published.cityWaterlooen
uws.published.countryCanadaen
uws.published.provinceOntarioen
uws.typeOfResourceTexten
uws.peerReviewStatusUnrevieweden
uws.scholarLevelGraduateen


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