Differentially Private Fair Division

TL;DR

This paper explores how to ensure fairness and privacy in resource allocation using differential privacy, providing algorithms for approximate fairness and showing limitations under stronger privacy notions.

Contribution

It introduces algorithms for differentially private fair division with specific adjacency notions and establishes negative results for stronger privacy models.

Findings

Algorithms for approximate envy-freeness and proportionality under certain privacy conditions

Negative results for fairness when entire utility functions can change

Differential privacy can be compatible with fair division under specific assumptions

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.