Themis: A Fair and Privacy-Preserving Outsourcing Platform Using Blockchain

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.