Differentially Private Fair Division
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.
Cite
Text
Manurangsi and Suksompong. "Differentially Private Fair Division." AAAI Conference on Artificial Intelligence, 2023. doi:10.1609/AAAI.V37I5.25721Markdown
[Manurangsi and Suksompong. "Differentially Private Fair Division." AAAI Conference on Artificial Intelligence, 2023.](https://mlanthology.org/aaai/2023/manurangsi2023aaai-differentially/) doi:10.1609/AAAI.V37I5.25721BibTeX
@inproceedings{manurangsi2023aaai-differentially,
title = {{Differentially Private Fair Division}},
author = {Manurangsi, Pasin and Suksompong, Warut},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2023},
pages = {5814-5822},
doi = {10.1609/AAAI.V37I5.25721},
url = {https://mlanthology.org/aaai/2023/manurangsi2023aaai-differentially/}
}