Presented by Josh Holmes
Computing PhD, Computer Science emphasis
Hybrid Presentation: Attend in-person at City Center Plaza Conference Room 352 or register to attend via Zoom
Dr. Gaby Dagher (Advisor), Dr. Jyh-haw Yeh, Dr. Min Long
In this dissertation, we delve into the field of secure multiparty computation, focusing on its applications in diverse domains such as peer-to-peer lending, fair exchange with cryptocurrencies, consensus mechanisms, and cryptographic voting. Our overarching goal is to design protocols that safeguard the privacy of inputs, ensure honest execution, and guarantee fairness in outcomes, particularly in scenarios where mutual trust among parties is absent. In the context of peer-to-peer lending, we introduce a platform that utilizes zero-knowledge proofs to establish unlinkability between lenders and borrowers while ensuring security against potential malicious behaviors. We also introduce a framework that addresses fair asset exchange across heterogeneous blockchain sets, preserving anonymity without requiring third-party involvement. Next, we introduce a consensus protocol that comprises quorum selection, block creation, and decentralized arbitration components, showcasing scalability, robustness, and fairness through experimental evaluations. Finally, we introduce a cryptographic voting protocol that employs hidden credentials and mutable identities to enable voters to submit indistinguishable dummy ballots, thus thwarting coercion and preserving privacy. The dissertation rigorously analyzes each protocol’s efficiency, scalability, and security, providing a comprehensive exploration of the applications of secure multiparty computation in ensuring privacy, honest behavior, and fairness across various domains.