Differentially Private Fair Division

Fuente: arXiv
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Bibliographic Details
Main Authors: Manurangsi, Pasin, Suksompong, Warut
Format: Preprint
Published: 2022
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author Manurangsi, Pasin
Suksompong, Warut
author_facet Manurangsi, Pasin
Suksompong, Warut
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2211_12738
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Differentially Private Fair Division
Manurangsi, Pasin
Suksompong, Warut
Computer Science and Game Theory
Cryptography and Security
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.
title Differentially Private Fair Division
topic Computer Science and Game Theory
Cryptography and Security
url https://arxiv.org/abs/2211.12738