Differentially Private Fair Division
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
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| _version_ | 1866916794331037696 |
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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 |