Differentially Private Fair Division
arXiv:2211.12738 · doi:10.1016/j.artint.2025.104385
Abstract
Fairness 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.
Appears in the 37th AAAI Conference on Artificial Intelligence (AAAI), 2023