FairHash: A Fair and Memory/Time-efficient Hashmap
Summary: FairHash: data-dependent hashmap with group-level statistical parity across buckets, giving formal guarantees for three fairness notions. Three algorithmic families span zero/uniform unfairness vs. memory overhead, with ranking/cut/discrepancy trade-offs and near-baseline performance. (summarized by gpt-5.4-mini on May 24 2026)
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Authors
- 1. Nima Shahbazi (University of Illinois Chicago)
- 2. Stavros Sintos (University of Illinois Chicago)
- 3. Abolfazl Asudeh (University of Illinois Chicago)
BibTeX Citation
@inproceedings{shahbazi_sigmod24,
title = {{FairHash: A Fair and Memory/Time-efficient Hashmap}},
author = {Shahbazi, Nima and Sintos, Stavros and Asudeh, Abolfazl},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3654939},
url = {https://dl.acm.org/doi/10.1145/3654939},
year = {2024}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,238 | Fair-Count-Min: Frequency Estimation under Equal Group-wise Approximation Factor | 2026 | SIGMOD | 5.093636e-05 |
| 10,511 | On Fair Epsilon Net and Geometric Hitting Set | 2026 | VLDB | 5.093636e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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