Differentially Private Fair Division

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.

Authors

• Pasin Manurangsi Google Research
• Warut Suksompong National University of Singapore

Keywords

• GTEP: Fair Division
• GTEP: Game Theory
• ML: Privacy-Aware ML