Publication Date
12-2024
Date of Final Oral Examination (Defense)
9-25-2024
Type of Culminating Activity
Thesis
Degree Title
Master of Science in Computer Science
Department
Computer Science
Supervisory Committee Chair
Gaby G. Dagher, Ph.D.
Supervisory Committee Member
Nasir Eisty, Ph.D.
Supervisory Committee Member
Eric Henderson, Ph.D.
Abstract
Outsourcing systems rely on the strategic selection of reputable, reliable, and trustworthy workers. Achieving fairness in task allocation is essential, as it directly correlates with increased network participation. Ensuring fairness requires evaluating a worker's reputation based on their past performance and behavior without compromising privacy. However, directly disclosing a worker's past performance can compromise privacy, making them vulnerable to targeted attacks, undue scrutiny, and potential biases. Furthermore, while transparency in performance assessments is crucial, it may incentivize malicious or self-serving behaviors, jeopardizing the integrity of the system. In this thesis, we present THEMIS, a blockchain-based outsourcing platform that utilizes a differentially private graph-based algorithm to select a subset of workers while preserving reputation privacy. THEMIS employs a privacy-preserving protocol to account for individual worker contributions without revealingtheir performance. Our experiments demonstrate the effectiveness of the proposed solution in selecting reputable nodes while preserving overall privacy. The findings confirm that the proposed algorithm achieves fairness, scalability, and efficiency.
DOI
https://doi.org/10.18122/td.2315.boisestate
Recommended Citation
Sigdel, Akshey, "Themis: A Fair and Privacy-Preserving Outsourcing Platform Using Blockchain" (2024). Boise State University Theses and Dissertations. 2315.
https://doi.org/10.18122/td.2315.boisestate
Comments
ORCID: 0009-0003-2525-8518