Differentially Private Fair Division
Keywords:GTEP: Fair Division, ML: Privacy-Aware ML, GTEP: Game Theory
AbstractFairness and privacy are two important concerns in social decision-making processes such as resource allocation. We study privacy in the fair allocation of indivisible resources using the well-established framework of differential privacy. We present algorithms for approximate envy-freeness and proportionality when two instances are considered to be adjacent if they differ only on the utility of a single agent for a single item. On the other hand, we provide strong negative results for both fairness criteria when the adjacency notion allows the entire utility function of a single agent to change.
How to Cite
Manurangsi, P., & Suksompong, W. (2023). Differentially Private Fair Division. Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 5814-5822. https://doi.org/10.1609/aaai.v37i5.25721
AAAI Technical Track on Game Theory and Economic Paradigms